{
 "cells": [
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    "# A Very Simple Introduction to Reinforcement Learning Concepts (Detailed)\n",
    "\n",
    "# Table of Contents\n",
    "\n",
    "- [1. Introduction: What is Reinforcement Learning?](#1-introduction)\n",
    "  - [1.1 Analogy: Learning by Trial and Error](#11-analogy)\n",
    "  - [1.2 Core Components](#12-core-components)\n",
    "    - [1.2.1 Agent](#121-agent)\n",
    "    - [1.2.2 Environment](#122-environment)\n",
    "    - [1.2.3 State (s)](#123-state)\n",
    "    - [1.2.4 Action (a)](#124-action)\n",
    "    - [1.2.5 Reward (r)](#125-reward)\n",
    "    - [1.2.6 Policy (π)](#126-policy)\n",
    "    - [1.2.7 Episode](#127-episode)\n",
    "    - [1.2.8 Goal](#128-goal)\n",
    "  - [1.3 The Agent-Environment Interaction Loop](#13-interaction-loop)\n",
    "- [2. The Task: A Simple Grid World](#2-the-task)\n",
    "  - [2.1 Environment Setup](#21-environment-setup)\n",
    "  - [2.2 States, Actions, Rewards Defined](#22-definitions)\n",
    "- [3. Our Simple \"Learning\" Agent: MemoryBot](#3-agent)\n",
    "  - [3.1 Agent's Goal vs. True RL Goal](#31-agent-goal)\n",
    "  - [3.2 The Agent's Memory Structure](#32-agent-memory)\n",
    "  - [3.3 The Agent's Simple Policy (Strategy)](#33-agent-policy)\n",
    "  - [3.4 The Agent's \"Learning\" Process (Memory Update)](#34-agent-learning)\n",
    "- [4. Setting up the Simulation Environment](#4-setup)\n",
    "  - [4.1 Importing Libraries](#41-imports)\n",
    "  - [4.2 Environment Class Implementation](#42-environment-class)\n",
    "  - [4.3 Instantiating the Environment](#43-instantiate-env)\n",
    "- [5. Implementing the Simple Agent's Logic](#5-implementation)\n",
    "  - [5.1 Initializing Agent Memory](#51-init-memory)\n",
    "  - [5.2 Choosing an Action (Epsilon-Greedy based on Immediate Reward)](#52-choose-action)\n",
    "  - [5.3 Updating Agent Memory](#53-update-memory)\n",
    "- [6. Running the Simulation Loop](#6-simulation)\n",
    "  - [6.1 Simulation Parameters](#61-parameters)\n",
    "  - [6.2 Initialization for the Run](#62-initialization)\n",
    "  - [6.3 The Main Simulation Loop (Detailed Steps)](#63-main-loop)\n",
    "- [7. Visualizing the Results](#7-visualization)\n",
    "  - [7.1 Basic Performance Metrics](#71-basic-plots)\n",
    "    - [7.1.1 Episode Rewards Over Time](#711-rewards)\n",
    "    - [7.1.2 Episode Lengths Over Time](#712-lengths)\n",
    "    - [7.1.3 Epsilon Decay](#713-epsilon)\n",
    "  - [7.2 Analyzing Agent's Knowledge and Behavior](#72-advanced-plots)\n",
    "    - [7.2.1 State Visitation Frequency](#721-state-visits)\n",
    "    - [7.2.2 Average Immediate Reward Map](#722-avg-reward-map)\n",
    "    - [7.2.3 Derived Greedy Policy Visualization](#723-policy-vis)\n",
    "    - [7.2.4 Action Selection Frequency (Example State)](#724-action-freq)\n",
    "    - [7.2.5 Convergence of Average Reward (Example State-Action)](#725-reward-convergence)\n",
    "- [8. Limitations of this Simple Agent](#8-limitations)\n",
    "- [9. Conclusion & Next Steps](#9-conclusion)\n",
    "\n",
    "## 1. Introduction: What is Reinforcement Learning?\n",
    "\n",
    "Reinforcement Learning (RL) is a fascinating area of Machine Learning focused on training intelligent **agents** to make optimal sequences of decisions within an **environment** to achieve a specific **goal**. Unlike supervised learning (where we have labeled data) or unsupervised learning (where we find patterns in data), RL agents learn through **trial and error**.\n",
    "\n",
    "### 1.1 Analogy: Learning by Trial and Error\n",
    "\n",
    "Think about how you might train a dog to sit. \n",
    "- The dog is the **agent**.\n",
    "- The room and your commands form the **environment**.\n",
    "- The dog being standing or sitting is its **state**.\n",
    "- The dog choosing to sit or stand is its **action**.\n",
    "- If the dog sits when you say \"sit\", you give it a treat (a positive **reward**).\n",
    "- If it doesn't sit, you might give no reward or a mild negative signal (negative reward).\n",
    "- Over time, the dog learns a **policy**: \"When my human says 'sit', the action 'sitting down' usually leads to a treat (good reward), so I should do that.\"\n",
    "\n",
    "RL works on similar principles, but often in much more complex scenarios.\n",
    "\n",
    "### 1.2 Core Components\n",
    "\n",
    "Let's formally define the key pieces:\n",
    "\n",
    "#### 1.2.1 Agent\n",
    "The entity we are training. It perceives the environment's state and decides which action to take.\n",
    "*(Our example: A simple program deciding where to move on a grid.)*\n",
    "\n",
    "#### 1.2.2 Environment\n",
    "The external system the agent interacts with. It defines the rules, physics, and outcomes of actions.\n",
    "*(Our example: The grid, walls, goal location, and how actions change the agent's position.)*\n",
    "\n",
    "#### 1.2.3 State ($s$)\n",
    "A complete description of the environment at a specific moment. The agent uses the state to decide its next action.\n",
    "*(Our example: The agent's `(row, col)` coordinates.)*\n",
    "\n",
    "#### 1.2.4 Action ($a$)\n",
    "One of the possible moves or decisions the agent can make.\n",
    "*(Our example: Move Up, Down, Left, or Right.)*\n",
    "\n",
    "#### 1.2.5 Reward ($r$)\n",
    "A scalar feedback signal from the environment received *after* performing an action $a$ in state $s$ and transitioning to state $s'$. It indicates the immediate desirability of the action taken in that state.\n",
    "*(Our example: +10, -1, or -0.1 depending on the outcome of the move.)*\n",
    "\n",
    "#### 1.2.6 Policy ($\\pi$)\n",
    "The agent's strategy. It defines how the agent chooses an action given a state. It can be deterministic (always choosing the same action for a state) or stochastic (choosing actions based on probabilities).\n",
    "*(Our example: An $\\epsilon$-greedy strategy based on remembering which actions yielded good immediate rewards from a state.)*\n",
    "\n",
    "#### 1.2.7 Episode\n",
    "A full sequence of agent-environment interactions, from a starting state until a terminal state is reached or a time limit is exceeded.\n",
    "*(Our example: A full attempt to navigate the grid from (0,0) to (9,9).)*\n",
    "\n",
    "#### 1.2.8 Goal\n",
    "The ultimate objective is usually to maximize the **return**, which is the cumulative sum of discounted rewards over an episode or potentially an infinite horizon. $\\text{Return} = \\sum_{t=0}^{T} \\gamma^t r_{t+1}$, where $\\gamma$ is a discount factor (0 to 1) that prioritizes immediate rewards over distant future rewards.\n",
    "*(Our example: Get the highest possible score by reaching the goal quickly and avoiding walls.)*\n",
    "\n",
    "### 1.3 The Agent-Environment Interaction Loop\n",
    "The core process of RL follows this cycle:\n",
    "1.  The agent observes the current state $s_t$ from the environment.\n",
    "2.  Based on $s_t$, the agent chooses an action $a_t$ according to its policy $\\pi$.\n",
    "3.  The agent performs action $a_t$.\n",
    "4.  The environment transitions to a new state $s_{t+1}$.\n",
    "5.  The environment provides a reward $r_t$ to the agent.\n",
    "6.  The agent uses the experience $(s_t, a_t, r_t, s_{t+1})$ to update its knowledge or policy (this is the \"learning\" step).\n",
    "7.  The loop repeats from the new state $s_{t+1}$.\n",
    "\n",
    "## 2. The Task: A Simple Grid World\n",
    "\n",
    "### 2.1 Environment Setup\n",
    "We'll use a 10x10 grid. The agent starts at `(0,0)` and needs to reach `(9,9)`. This simple setup allows us to clearly see states, actions, and their consequences.\n",
    "\n",
    "### 2.2 States, Actions, Rewards Defined\n",
    "- **State:** Agent's `(row, col)` tuple.\n",
    "- **Actions:** 4 discrete actions: 0 (Up), 1 (Down), 2 (Left), 3 (Right).\n",
    "- **Transitions:** Deterministic. Taking action 'Up' from `(r, c)` leads to `(r-1, c)` unless `r=0` (wall).\n",
    "- **Rewards:**\n",
    "    - +10: Reaching the goal `(9, 9)`.\n",
    "    - -1: Bumping into a wall (moving off-grid).\n",
    "    - -0.1: Taking any other step.\n",
    "- **Episode End:** Reaching the goal or exceeding a step limit.\n",
    "\n",
    "## 3. Our Simple \"Learning\" Agent: MemoryBot\n",
    "\n",
    "### 3.1 Agent's Goal vs. True RL Goal\n",
    "A true RL agent aims to maximize the *cumulative discounted reward*. Our simple 'MemoryBot' agent will have a much simpler objective: it will try to pick actions that have led to high *average immediate rewards* from the current state in the past. It has no concept of future rewards or discounting. This highlights the difference between basic memory and true value-based learning.\n",
    "\n",
    "### 3.2 The Agent's Memory Structure\n",
    "We'll use a nested dictionary: `memory[state][action] -> list_of_rewards`.\n",
    "- `state` is a `(row, col)` tuple.\n",
    "- `action` is an integer (0-3).\n",
    "- `list_of_rewards` stores all the immediate rewards received *immediately after* taking that `action` from that `state`.\n",
    "\n",
    "### 3.3 The Agent's Simple Policy (Strategy)\n",
    "$\\epsilon$-greedy based on average immediate reward:\n",
    "1.  Generate a random number `rand` between 0 and 1.\n",
    "2.  If `rand < epsilon`: Choose a random action (0, 1, 2, or 3).\n",
    "3.  If `rand >= epsilon`:\n",
    "    a.  For the current state `s`, calculate the average immediate reward for each possible action `a`: `AvgR(s, a) = mean(memory[s][a])` (treat empty lists as having an average of 0 or a very small negative number).\n",
    "    b.  Find the action(s) `a*` with the highest `AvgR(s, a*)`.\n",
    "    c.  Choose one of these best actions randomly (to break ties).\n",
    "\n",
    "### 3.4 The Agent's \"Learning\" Process (Memory Update)\n",
    "This is just bookkeeping:\n",
    "- After taking action $a_t$ in state $s_t$ and receiving reward $r_t$, simply append $r_t$ to the list `memory[s_t][a_t]`.\n",
    "\n",
    "## 4. Setting up the Simulation Environment\n",
    "\n",
    "### 4.1 Importing Libraries\n",
    "Import the necessary Python libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Libraries imported and seeds set.\n"
     ]
    }
   ],
   "source": [
    "# Import necessary libraries\n",
    "import numpy as np # For numerical operations (like mean)\n",
    "import matplotlib.pyplot as plt # For plotting results\n",
    "import random # For random choices (exploration, tie-breaking)\n",
    "from collections import defaultdict # Convenient for creating nested dictionaries for memory\n",
    "from typing import Tuple, Dict, List, DefaultDict, Any # For type hinting\n",
    "\n",
    "# Set random seeds for reproducibility\n",
    "seed: int = 42\n",
    "random.seed(seed)\n",
    "np.random.seed(seed)\n",
    "\n",
    "# Enable inline plotting for Jupyter Notebook\n",
    "%matplotlib inline\n",
    "\n",
    "print(\"Libraries imported and seeds set.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Environment Class Implementation\n",
    "Define the Grid World environment class. We add comments explaining each part."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GridEnvironmentSimple:\n",
    "    \"\"\" \n",
    "    Basic Grid World environment for demonstrating RL concepts.\n",
    "    States are (row, col) tuples. Actions are integers 0-3.\n",
    "    \"\"\"\n",
    "    def __init__(self, rows: int = 10, cols: int = 10) -> None:\n",
    "        \"\"\" Initialize the grid environment. \"\"\"\n",
    "        self.rows: int = rows # Number of rows\n",
    "        self.cols: int = cols # Number of columns\n",
    "        self.start_state: Tuple[int, int] = (0, 0) # Agent starts at top-left\n",
    "        self.goal_state: Tuple[int, int] = (rows - 1, cols - 1) # Goal at bottom-right\n",
    "        self.state: Tuple[int, int] = self.start_state # Agent's current position\n",
    "        # Define action mapping: 0=Up, 1=Down, 2=Left, 3=Right\n",
    "        self.action_map: Dict[int, Tuple[int, int]] = {\n",
    "            0: (-1, 0), 1: (1, 0), 2: (0, -1), 3: (0, 1)\n",
    "        }\n",
    "        self.actions: List[int] = list(self.action_map.keys()) # List of possible action indices\n",
    "        self.action_dim: int = len(self.actions) # Number of possible actions\n",
    "        print(f\"[Env] Initialized {rows}x{cols} grid. Start: {self.start_state}, Goal: {self.goal_state}\")\n",
    "\n",
    "    def reset(self) -> Tuple[int, int]:\n",
    "        \"\"\" Resets the agent to the start state. Returns the initial state. \"\"\"\n",
    "        self.state = self.start_state\n",
    "        # print(f\"[Env] Reset to state: {self.state}\") # Debug\n",
    "        return self.state\n",
    "\n",
    "    def step(self, action: int) -> Tuple[Tuple[int, int], float, bool]:\n",
    "        \"\"\"\n",
    "        Executes an action, updates the state, and returns the outcome.\n",
    "        \n",
    "        Returns:\n",
    "            (next_state, reward, done)\n",
    "        \"\"\"\n",
    "        # If already at goal, episode effectively ends (return current state, 0 reward, True)\n",
    "        if self.state == self.goal_state:\n",
    "            # print(f\"[Env] Already at goal {self.state}. No action taken.\") # Debug\n",
    "            return self.state, 0.0, True\n",
    "        \n",
    "        # Get state change from action map\n",
    "        dr, dc = self.action_map[action]\n",
    "        current_row, current_col = self.state\n",
    "        \n",
    "        # Calculate potential new position\n",
    "        next_row, next_col = current_row + dr, current_col + dc\n",
    "        \n",
    "        # Default reward is the step cost\n",
    "        reward: float = -0.1\n",
    "        \n",
    "        # Check boundaries (walls)\n",
    "        if not (0 <= next_row < self.rows and 0 <= next_col < self.cols):\n",
    "            # Hit wall: state doesn't change, reward is penalty\n",
    "            next_state_tuple = self.state \n",
    "            reward = -1.0 \n",
    "            # print(f\"[Env] Wall hit at {self.state} trying action {action}. Next state: {next_state_tuple}, Reward: {reward}\") # Debug\n",
    "        else:\n",
    "            # Valid move: update internal state\n",
    "            self.state = (next_row, next_col)\n",
    "            next_state_tuple = self.state\n",
    "            # print(f\"[Env] Moved from {(current_row, current_col)} via action {action} to {self.state}. Reward: {reward}\") # Debug\n",
    "        \n",
    "        # Check if the new state is the goal\n",
    "        done: bool = (self.state == self.goal_state)\n",
    "        if done:\n",
    "            reward = 10.0 # Assign goal reward\n",
    "            # print(f\"[Env] Goal reached! State: {self.state}. Reward: {reward}, Done: {done}\") # Debug\n",
    "            \n",
    "        # Return the outcome: new state, reward, done flag\n",
    "        return next_state_tuple, reward, done\n",
    "\n",
    "    def get_action_space_size(self) -> int:\n",
    "        \"\"\" Returns the number of possible actions. \"\"\"\n",
    "        return self.action_dim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Instantiating the Environment\n",
    "Create an instance of our grid world."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Env] Initialized 10x10 grid. Start: (0, 0), Goal: (9, 9)\n"
     ]
    }
   ],
   "source": [
    "# Create the environment instance\n",
    "simple_env: GridEnvironmentSimple = GridEnvironmentSimple(rows=10, cols=10)\n",
    "# Get the number of possible actions from the environment\n",
    "n_actions_simple: int = simple_env.get_action_space_size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Implementing the Simple Agent's Logic\n",
    "\n",
    "Implement the functions for memory, action selection, and learning (memory update).\n",
    "\n",
    "### 5.1 Initializing Agent Memory\n",
    "We use `defaultdict` for convenience. If we access `memory[state]` and it doesn't exist, it automatically creates a new `defaultdict(list)`. If we access `memory[state][action]` and it doesn't exist, it automatically creates an empty `list`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Agent memory initialized.\n",
      "Example memory access for unvisited state (0,1), action 0: []\n"
     ]
    }
   ],
   "source": [
    "# Agent's memory: Stores lists of immediate rewards.\n",
    "# Key: state tuple (r, c)\n",
    "# Value: Another defaultdict where Key: action index (0-3), Value: List of rewards [float]\n",
    "agent_memory: DefaultDict[Tuple[int, int], DefaultDict[int, List[float]]] = \\\n",
    "    defaultdict(lambda: defaultdict(list))\n",
    "\n",
    "print(\"Agent memory initialized.\")\n",
    "print(f\"Example memory access for unvisited state (0,1), action 0: {agent_memory[(0,1)][0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 Choosing an Action (Epsilon-Greedy based on Immediate Reward)\n",
    "This function implements the agent's policy $\\pi(a|s)$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def choose_simple_action(state: Tuple[int, int], \n",
    "                           memory: DefaultDict[Tuple[int, int], DefaultDict[int, List[float]]], \n",
    "                           epsilon: float, \n",
    "                           n_actions: int) -> int:\n",
    "    \"\"\"\n",
    "    Chooses action using epsilon-greedy based on average immediate rewards in memory.\n",
    "    \"\"\"\n",
    "    # Decide whether to explore or exploit\n",
    "    if random.random() < epsilon:\n",
    "        # --- Exploration --- \n",
    "        action: int = random.randrange(n_actions) # Choose a random action index (0, 1, 2, or 3)\n",
    "        # print(f\"  -> Exploring: Random action = {action}\") # Debug\n",
    "        return action\n",
    "    else:\n",
    "        # --- Exploitation --- \n",
    "        # Calculate the average reward remembered for each possible action from the current state\n",
    "        avg_rewards_for_actions: List[float] = []\n",
    "        state_action_memory: DefaultDict[int, List[float]] = memory[state]\n",
    "        \n",
    "        for a in range(n_actions):\n",
    "            rewards_list: List[float] = state_action_memory[a]\n",
    "            if not rewards_list: # If we have no memory for this action in this state\n",
    "                avg_reward: float = 0.0 # Assign a default value (could be negative)\n",
    "            else:\n",
    "                avg_reward = np.mean(rewards_list) # Calculate the average of remembered rewards\n",
    "            avg_rewards_for_actions.append(avg_reward)\n",
    "            \n",
    "        # Find the highest average reward among all actions\n",
    "        max_avg_reward: float = max(avg_rewards_for_actions)\n",
    "        \n",
    "        # Get a list of all actions that achieve this maximum average reward\n",
    "        best_actions: List[int] = [a for a, avg_r in enumerate(avg_rewards_for_actions) if avg_r == max_avg_reward]\n",
    "        \n",
    "        # Randomly choose one action from the best actions (breaks ties)\n",
    "        action = random.choice(best_actions)\n",
    "        # print(f\"  -> Exploiting: Avg Rewards={avg_rewards_for_actions}, Best Action(s)={best_actions}, Chosen={action}\") # Debug\n",
    "        return action"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3 Updating Agent Memory\n",
    "This function represents the agent's simple \"learning\" process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_simple_memory(memory: DefaultDict[Tuple[int, int], DefaultDict[int, List[float]]], \n",
    "                           state: Tuple[int, int], \n",
    "                           action: int, \n",
    "                           reward: float) -> None:\n",
    "    \"\"\"\n",
    "    Updates the agent's memory by adding the received immediate reward.\n",
    "    \"\"\"\n",
    "    # Access the list of rewards for the specific state-action pair and append the new reward\n",
    "    memory[state][action].append(reward)\n",
    "    # print(f\"  -> Memory Updated: State={state}, Action={action}, Added Reward={reward:.1f}, New Memory for Action: {memory[state][action]}\") # Debug"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Running the Simulation Loop\n",
    "\n",
    "We simulate the agent interacting with the environment over multiple episodes.\n",
    "\n",
    "### 6.1 Simulation Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Simulation Hyperparameters\n",
    "NUM_EPISODES: int = 1000\n",
    "MAX_STEPS_PER_EPISODE: int = 150\n",
    "EPSILON_START_SIM: float = 1.0  # Start with fully random exploration\n",
    "EPSILON_END_SIM: float = 0.01   # Keep a small chance of exploration\n",
    "EPSILON_DECAY_SIM: float = 0.99 # Decay rate per episode (e.g., 0.99 means epsilon becomes 99% of its previous value)\n",
    "PRINT_EVERY_N_EPISODES: int = 100 # How often to print progress"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2 Initialization for the Run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Env] Initialized 10x10 grid. Start: (0, 0), Goal: (9, 9)\n",
      "Simulation setup complete.\n"
     ]
    }
   ],
   "source": [
    "# Create environment instance for the simulation run\n",
    "env_run: GridEnvironmentSimple = GridEnvironmentSimple(rows=10, cols=10)\n",
    "n_actions_run: int = env_run.get_action_space_size()\n",
    "\n",
    "# Initialize Agent Memory for this run\n",
    "agent_memory_run: DefaultDict[Tuple[int, int], DefaultDict[int, List[float]]] = \\\n",
    "    defaultdict(lambda: defaultdict(list))\n",
    "\n",
    "# Initialize lists to store results for plotting\n",
    "simple_episode_rewards: List[float] = []\n",
    "simple_episode_lengths: List[int] = []\n",
    "simple_episode_epsilons: List[float] = []\n",
    "\n",
    "# Initialize exploration rate for this run\n",
    "current_epsilon_run: float = EPSILON_START_SIM\n",
    "\n",
    "print(\"Simulation setup complete.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.3 The Main Simulation Loop (Detailed Steps)\n",
    "The agent interacts with the environment episode by episode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting Simple Agent Simulation for 1000 episodes...\n",
      "Episode 100/1000 | Avg Reward (last 100): -20.71 | Avg Length: 144.0 | Epsilon: 0.366\n",
      "Episode 200/1000 | Avg Reward (last 100): -17.11 | Avg Length: 148.6 | Epsilon: 0.134\n",
      "Episode 300/1000 | Avg Reward (last 100): -14.65 | Avg Length: 144.3 | Epsilon: 0.049\n",
      "Episode 400/1000 | Avg Reward (last 100): -15.26 | Avg Length: 149.7 | Epsilon: 0.018\n",
      "Episode 500/1000 | Avg Reward (last 100): -15.07 | Avg Length: 149.7 | Epsilon: 0.010\n",
      "Episode 600/1000 | Avg Reward (last 100): -11.95 | Avg Length: 136.8 | Epsilon: 0.010\n",
      "Episode 700/1000 | Avg Reward (last 100): -10.14 | Avg Length: 127.4 | Epsilon: 0.010\n",
      "Episode 800/1000 | Avg Reward (last 100): -11.02 | Avg Length: 133.2 | Epsilon: 0.010\n",
      "Episode 900/1000 | Avg Reward (last 100): -13.35 | Avg Length: 141.3 | Epsilon: 0.010\n",
      "Episode 1000/1000 | Avg Reward (last 100): -7.49 | Avg Length: 110.5 | Epsilon: 0.010\n",
      "\n",
      "Simple Agent Simulation Finished after 1000 episodes.\n"
     ]
    }
   ],
   "source": [
    "print(f\"Starting Simple Agent Simulation for {NUM_EPISODES} episodes...\")\n",
    "\n",
    "# Loop over the specified number of episodes\n",
    "for i_episode in range(1, NUM_EPISODES + 1):\n",
    "    \n",
    "    # --- Start of Episode --- \n",
    "    # Reset the environment to get the starting state\n",
    "    current_state: Tuple[int, int] = env_run.reset()\n",
    "    # Reset episode-specific trackers\n",
    "    current_episode_reward: float = 0.0\n",
    "    \n",
    "    # print(f\"\\n--- Episode {i_episode} Start | Epsilon: {current_epsilon_run:.3f} ---\") # Debug\n",
    "\n",
    "    # Loop for each step within the episode, up to the maximum allowed steps\n",
    "    for t in range(MAX_STEPS_PER_EPISODE):\n",
    "        \n",
    "        # print(f\" Step {t+1}: State={current_state}\") # Debug\n",
    "        \n",
    "        # 1. Agent chooses an action\n",
    "        action: int = choose_simple_action(\n",
    "            current_state, \n",
    "            agent_memory_run, \n",
    "            current_epsilon_run, \n",
    "            n_actions_run\n",
    "        )\n",
    "        \n",
    "        # 2. Agent takes action in the environment\n",
    "        next_state, reward, done = env_run.step(action)\n",
    "        # print(f\"   Action={action}, Reward={reward:.1f}, NextState={next_state}, Done={done}\") # Debug\n",
    "        \n",
    "        # 3. Agent updates its memory (learns from immediate reward)\n",
    "        update_simple_memory(agent_memory_run, current_state, action, reward)\n",
    "        \n",
    "        # Update the state for the next time step\n",
    "        current_state = next_state\n",
    "        # Add the received reward to the episode's total reward\n",
    "        current_episode_reward += reward\n",
    "        \n",
    "        # Check if the episode terminated (goal reached or maybe other conditions)\n",
    "        if done:\n",
    "            # print(f\" Episode {i_episode} finished at step {t+1}. Goal Reached.\") # Debug\n",
    "            break # Stop the current episode\n",
    "            \n",
    "    # --- End of Episode --- \n",
    "    # If the loop finished because MAX_STEPS was reached\n",
    "    # if not done:\n",
    "        # print(f\" Episode {i_episode} finished at step {t+1}. Max steps reached.\") # Debug\n",
    "\n",
    "    # Store results for this episode\n",
    "    simple_episode_rewards.append(current_episode_reward)\n",
    "    simple_episode_lengths.append(t + 1) # t is the last step index, so length is t+1\n",
    "    simple_episode_epsilons.append(current_epsilon_run)\n",
    "    \n",
    "    # Decay epsilon for the next episode\n",
    "    current_epsilon_run = max(EPSILON_END_SIM, current_epsilon_run * EPSILON_DECAY_SIM)\n",
    "    \n",
    "    # Print progress summary periodically\n",
    "    if i_episode % PRINT_EVERY_N_EPISODES == 0:\n",
    "        avg_reward = np.mean(simple_episode_rewards[-PRINT_EVERY_N_EPISODES:])\n",
    "        avg_length = np.mean(simple_episode_lengths[-PRINT_EVERY_N_EPISODES:])\n",
    "        print(f\"Episode {i_episode}/{NUM_EPISODES} | Avg Reward (last {PRINT_EVERY_N_EPISODES}): {avg_reward:.2f} | Avg Length: {avg_length:.1f} | Epsilon: {current_epsilon_run:.3f}\")\n",
    "\n",
    "print(f\"\\nSimple Agent Simulation Finished after {NUM_EPISODES} episodes.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Visualizing the Results\n",
    "\n",
    "Plotting helps understand how the agent's performance changed over time.\n",
    "\n",
    "### 7.1 Basic Performance Metrics\n",
    "\n",
    "#### 7.1.1 Episode Rewards Over Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kk08ackssX75ceBtcfPHFtr/52Mc+JrxDSBBuLj+9prwVdnR2dgqlCgnWKWya+fHtb39b5MCQOWzIW4A8IP7617/qxyChr1lZQwqcs846C3/+858tFVUk7M2FyspKIUy2Ytu2beKRDwsXLhR/f/e73xnep9BOxCWXXJKxLHQvzPeBvKPMOUPyhYTjH//4x3HfffcJDxU6L72mPC6UfyVTnZPCpbu7W1wreclIhQC9T7lh9u/fb8jfIj2QzNdmrqt0kCCflCAUtkw9TjbHcNJPCtX+coHqkRRjkpaWFvzv//6v8BCyCicmvxOJRAzvkeKFPKXonmXTVrOpc/JgojxWlDvGDLV1c5kYhmEYhmEYb8EeLgzDMAzDMIxnoKTS5LlBCa7J2p/yuJBV+UMPPSQs0SmsWKGhMEck/P7GN76hC8Mp74ddiB8peCeL9+uvv17khKBwU+TpQPlDSNBLyb0ptJIVpEghhQrlh7CCEqKT5wR5wXzqU58Sx6YE5ZSYmyz3qZ7oGJTbg1Ct+EkJQ9dCgmIKfUXW9WR9TwLdvXv3ihw52XLiiSfiT3/6k7heqisSrJ9zzjnis3PPPVf8pTrIlTlz5ohwXJQDhQTMZ555phDm33///eLayXMlU1lIeXXLLbeI9nL66adjzZo1ov5U7wK3oNBelLeE2gl5vNCDFIcULozqnHIO0b2hhPbkySLvEzF37lwEg0Fs2rRJtBHJggULxHURqsKFkr3TZ3feeadQulH+Ewp1peb9yQS1JWqLFBqL6omUBpQ8nsLhZeP946SfFKL9qVCd/utf/7Lsj1S3EvI6+9KXvoR3331XhGy79957RTn+8Y9/pA3zR8rOyy+/XPyeFB2kBJNKtWzaajZ1Tu2J+jN9j8KZURtvb28XbZg8t6hvuemlxTAMwzAMw7hMnGEYhmEYhmE8wnPPPRf/4he/GJ8xY0a8qqoqXlJSEp86dWr8O9/5Try+vt7w3QkTJsSvuuoq/fU//vEPMh2Pv/vuu4bv3XTTTeL9xsZGw/v028rKSv31jh07xPfuuuuu+N133x0fN25cvLS0ND5//vz4qlWrLI9p5rHHHovPmzdPHJcedB3f+ta34ps2bbK95g996EPxsrKyeHt7u+13Pv/5z8dDoVD8wIED4jVdy2c/+9n4gAED4gMHDhSfv/XWW6JMDz74oOG327Zti1955ZXxkSNHimOMGTMmfumll8YfffTRjHX36quvivfpr6Suri5+ySWXiHPTZ2eeeabhntAjG6i+6ThU/5JwOBy/+eab45MmTRJlpntx/fXXx7u6ugy/tSsLfe+HP/xhfNSoUfHy8vL4GWecEV+6dKn4XC2vvOd0/emQ9fDII49Yfn7WWWfFq6ur401NTeI1tVW671RuKj/V/bnnnhv/y1/+kvLbk08+WRx72bJl+nt79+4V79HvzdBnH/3oR+M1NTXi3l9++eXx/fv3i+9Tu8zU7oloNCrqV9YPlX/t2rUpfcqKbPpJvu0vUxnsHuo10DVRG3nhhRfis2fPFmWlfmm+l+a2vn37djEWTZkyRfTPwYMHx88+++z4Sy+9ZPid07aaTZ23traKY9DYR2Pg0KFD46effnr817/+dbynp8dRHTEMwzAMwzB9g4/+c1uJwzAMwzAMwzDFBlmOUw6Wu+66y9YbxctQiCfyDHrzzTdFngmGKQTF1k/IM+7YY4/FM88809dFYRiGYRiGYY4AOIcLwzAMwzAMwxQZlPdFhZK0U34ICjl1wgkn9Fm5GIZhGIZhGIZhjmQ4hwvDMAzDMAzDFGGuG1K6UJ4Kyp/x+OOPi1w3v/jFL1BeXt7XxWMYhmEYhmEYhjkiYYULwzAMwzAMwxQZlBj+7rvvFmGSurq6RAJz8nChJN8MwzAMwzAMwzBM38A5XBiGYRiGYRiGYRiGYRiGYRiGYfKEc7gwDMMwDMMwDMMwDMMwDMMwDMPkCStcGIZhGIZhGIZhGIZhGIZhGIZhjqQcLq+//jruuusuvP/++6itrcUTTzyByy67TP+coqPddNNN+Otf/4qmpiacccYZ+NOf/oRp06Y5PkcsFsP+/fsxYMAA+Hy+Al0JwzAMwzAMwzAMwzAMwzAMwzDFAOkeWltbMXr0aPj9/v6hcGlvb8ecOXPwxS9+ER/72MdSPr/zzjvx+9//Hvfffz8mTZqEG2+8ERdeeCHWr1+PsrIyR+cgZcu4ceMKUHqGYRiGYRiGYRiGYRiGYRiGYYqVPXv2YOzYsbaf++KkmilCyPtE9XChyyDt0g9/+ENcc8014r3m5maMGDEC9913Hz796U87Oi79pqamRlRcdXV1Qa+hmAiHw1i8eDEuuOAChEKhvi4OwzD9EB5nGIYpNDzOMAxTaHicYRim0PA4wzBMoeFxxpqWlhbhqEGRtQYOHNg/PFzSsWPHDtTV1eG8887T36MLP/XUU7F06VJbhUt3d7d4SMgtiCgvLxcPRiMYDKKiokLUCXc0hmEKAY8zDMMUGh5nGIYpNDzOMAxTaHicYRim0PA4Y6+IIjKlIek3ChdSthDk0aJCr+VnVtxxxx24+eabU94nLR41LMbIiy++2NdFYBimn8PjDMMwhYbHGYZhCg2PMwzDFBoeZxiGKTQ8zhjp6OiAE/qNwiVXrr/+elx99dUprkHkMsUhxYwaPOpk559/Pms2GYYpCDzOMAxTaHicYRim0PA4wzBMoeFxhmGYQsPjjDWkNziiFC4jR44Uf+vr6zFq1Cj9fXp93HHH2f6utLRUPMxQY+IGlQrXC8MwhYbHGYZhCg2PMwzDFBoeZxiGKTQ8zjAMU2h4nDHitC786CdMmjRJKF1efvllg9Zp2bJlmDt3bp+WjWEYhmEYhmEYhmEYhmEYhmGY/k1Rebi0tbVh69at+usdO3Zg5cqVGDx4MMaPH4/vf//7uO222zBt2jShgLnxxhsxevRoXHbZZX1aboZhGIZhGIZhGIZhGIZhGIZh+jdFpXB57733cPbZZ+uvZe6Vq666Cvfddx9+9KMfob29HV/96lfR1NSEefPm4fnnn0dZWVkflpphGIZhGIZhGIZhGIZhGIZhmP5OUSlczjrrLMTjcdvPfT4fbrnlFvFgGIZhGIZhGIZhGIZhGIZhmCOBF9fX480tjfrrsYMqcNnxYzBsQGr+8s31rXj43T0IR2M4dfIQLJyl5UTvicSANPJ3pp8pXBiGYRiGYRiGYRiGsaetO4LKkoAwSGQYhmGY/sx7Ow/h/5bvxjfOnIJpIwbAa/NxValR9B6JxlDb3IVgwIeR1WWGuZo+64rEUn6TDc+trUVzR1h/vfdwJ97fdRg3fugYjKkpN3z39c2N2HGgXTzf98E+nDZ5CAZXluAHD69Ed08ECypzLsYRj7+vC8AwDMMwDMMwDMMwTP6Q4OR7D6zAH5ds6+uiMAzDMEzBWbuvWSgYNtS1FvxcLV1hbG1wdp6H39sj5uMlmxoM7//+5S34+dPr8NMn1uKWZ9Ybjvfblzbj6odWorG1W/c02VDbgmjMubeJ8E4B8MV5k/Ddc6dh7KBy4cHywa7DKd9t747qzymi1KsbG8Tf7nAU0XgcIdYa5AxXHcMwDMMwDMMwDJMXH+w+jFV7mvq6GEc8z6+tE3+tBCtMdlbJi9fVGayEGYZhGO8RTegiYlkoJXLl3jd34I5FG7H7YEfa75HnyAuJ+XiToggiZcbm+jbxPOD3iePQ8f782jYcau/BxtpWoVzZklDCkLfKr1/YhL+8vl38lpQpi9bUCmXOsu0HLdNuRBIVMn3kAMwZV4MTJgwSrw+0aUoclfaeiPg7d8oQ8fe1zY1o6Yro0cRKWGuQMxxSjGEYhmEYhmEYhsmZrnAUf3hlq3j+h/93AspCgb4uUlHx7OpahAI+XDBzZN7HonAkXoAEOw+9uwfdkRjG1pTj4lkjMaAshGKBhF/r97dg1d4mXHvhDBwpNHX04PEP9uGcGcMxcSjHkmEYxvtIpUOsF3KOHG7v0f529GD8kArb7z29ar/+vKIkuSZq7Y4IbxOKIvbLj8/G0yv34c2tB7B8xyER+ksyoFSbLw+09ehh0x4bUAq/T1szSA629+h5V2RdRGLaOiBIXwYwtErL3UIKHSvjAuL0KUOxsa5VXJ80ngn4fAhwZNKcYV0VwzCuk427I8MwDMMwDFOc0Mae1n0kVJdYbegZezp6Inj8g73CWlWGAcmHcB+sw6kNmK1sn1yxT3jZrNvXjBfW1eGGJ9aioaULxQIpWwiyNj6SIKHfW1sP4KUN9X1dFKbI9vs0BlhZ2zNMb7XH3pBDUZgt9a8dnT3JUF1quQ4mFCjV5SGRK+XzZ0zCV+ZPFu/tb0oqXKTySF0XPLemFovWaF4zs8YOFH8fe38vrn54Jf71zi79XLJowYAm8h9SVWJQ3qh0JBQuA8qCIp+M6glDxjOcCi53WOHCMIyrbGtsw7f/7wO8uJ4X6QzDMAzDMJkgAVVtc2dRGqz87Y0d+OHDKw1hl6QwgXGGFKaQgKQrkhTQ5ApZzvYm5FHz0yfX4s4XNunvUXsgwT3xsRPGYnh1mRDqvL3tIIqNcsUy+UhAKk9VYSHDSOqau/DbFzfjG/96X+SVUK3kb/7Petz41FrPeNkxRw7RXvRwiTkMX6aWRf3qwYQyY1jC64Q4eeJg3QvFfE1yjTA6keye1owzRg3A986dhgsTXrE051LulfbuCCLKychzlhhSWaqfW1WK0vP2xFhfWRrU5ztpOHOkzX9uwwoXhmFcZUdju5gUNtcfWdZgDMMwDMMwufDW1oMiceo/3tqBYmPd/ma0dkWw61C7/t6B9tQY4Yw9YRl8nkKzuSDk7m1hZ0Nrt/Bc2VzXKrx1iNe2NAoF4pThVbhk9iicNX2YeL+2uXg8XCQ1Fb0TBo0EX5QHySrGfm8iFXY9LDT3HLTHJkUmCVX7Ahpb7l68SSQop/6940C73mb+55Wt2HOoA7VNXWJOYJjeROoQesNuJRm+LP33VCMaVfkivUzIu0Xi9/twztHDDb+XCh05JlPYMJpLB1aE8P9OnQCfz4dPnjwOd3x8lu7BQn3SoHDxayL/QRUh4alCZWruDBsU7PI8laUBPfQZhSkjODxsfrDChWEYV5GaeHYnZhiGYRiGycwTK/aJv0uLzPqf1nodCQVBe3c0rYfLuzsP4dVNDbw+tEDGWic6w254uPRdHR9o1e790m0HxN9zZ2gCpFEDtTAldc3JcCleRlVaDa5ICsUKyXu7Dos8SD9+dDX6EplsWQ0TyHiDB5bvFrmF/vrG9j45P1m9qyEjpRB55Z4mbFGMLTOFWmKYog4p5vBcqpLFEFIsYZRi9mg586hhOHnS4JTfSIVLSdCPK+ZOxG8+eZzu7UIMH1CGKcOqxPPtB9oRTozdpJAhRY4MLVZTkRpWTCpvA34fSgL+pMIlofgvD7HKIB+49hiG6RONP8MwDMMwDENC9uK0BiaBrBQISM8GdaMuIevJe5Zsw7+W7sKTKzXlEpMq4CakAquYQoqpgvnGti6hrGhs1drA9JEDxN+RCYVLfUt3xjAsXuBQR1IgRWFWegNKVuwFpGdLtwvKv0xsqmvFf7+8pc+9eoqF1zc3ir9r9jb3yfnN+R/k+N9m8mgphj7O9C9kW+yNkGJyik13Li2fUfK12iWkYYL0SpGQN8nXz5yCY0ZXG34j51hSiNgxOaFwoWgz4YQRhwwnJpEKHnWNJo1lqkqDQkFTXqLNd4cTYWLZwyU/WOHCMEyvT0AMwziDFmscQ5thGKZ/0x0uTktyVTmghriRoSisvDaeWVVriPufD/1ljlQVJF2ueLj0bntSz9fYqlnA0zYgFPBjYLkWjmtoZSmCAZ/4rrl9uAWFSbnhiTV4/IO9eR9L9dLqLdlxMGGJ3Nf0ZkixO5/fKLwj7ntrZ8HPxeSPtMyXyP2+2dKf9S1Mb2PXFgt5rnTyrtQ+EdfneNmPZF4VM/5Elnr5mx7Fw8WOycMqxd/tB9p0Iw6ag1WGJhQ86hzcnjCWqSjVFCsVCQWLVJqWsYdLXnDtMQxToIRlfV0Shil+7nltO37w0Eo0KZaWDMMwDOMFVK8WmXSVMFurm8NkuZXnr7/MkWoIMFlXrV1h7D7YkdPx1PjtvYFM6Es0tnWLBzF0QImwmCUorMnIahlWrDB5XB5cvhv1zV14dnWtywqX3qnPQKKuCg0lV65NE9pNDynWi4pg1aOIyUxveV1JSIlKuSHM4SLtDC05pBjT2ySVIPBESDHzR6TAeHLFPnz3gRXYd7hTnyOdKFxkiLB0CpdxgypEWDDyNpO50sjIQUV61KgeLnIdV5nwbJEhxSRSAcPkRu+O1AzD9HukNpxjdDNM/uw+1C4sDSkEh4y76jW2NrSiqSOMkyYmY84yDMMw/R87DxcS6NLcJa0rO5T8Lm5aoMo5sq6ly7NzZLY5XMj6ldbQdy/ejL2HO/CzS2di/JAKT+dwUT1cDrR26+HEhlVpChbJyIHl2Hu4Uwj7Z40d6Ho5VrsYZkm15O8Ni2mChGWFhtrW1Q+vFM9/++njUF2meSCpyHA0vZnDJeQR755iQXqO9UZ7eWrlfixaUyv6wYQhmhW9ed+fYs3PlpdMLyOnod6QQTnxcLFSQj67ptbQNwZXWq9bpGOK7FfSw8UcIkyFlDHjBldg54F2bGtos/SalB41xhwuUYMSt9ykcOGQYvnBHi4Mw/T6BMQwjPcSAObKn5Zsx5+WbCt6C2OGYZi+ppcM3AuicDHnHjmshKwwe7i4NaUVwxyZbQ6Xzp4Y1te2YM+hDhGWa/W+pqyPJ61h+8zDRSpcBhjDpYxK5HEhBVkhyuBGODaJKpDqTwqXzfWaIE4qx6wIRxIeLpGoUPr9851dBV/jUUJnJj1q++4thcuWhjb8Z9V+vQ/sOthuEBTrYZzMwuUiH5OZ4iPeByHF0kVdtAqzN3moUWFZGrRWZshE97JbSaOGdB4uRE1iXKDwmlbjquy3h5XxXBrLSM+WioSni6ScFS55wTMbwzAFSljW1yVhGCMPvbsbf35tW1F5X8l+5GUFplyomQVqDMMwTHYUmyVhh+LVooYXI2RYKavP3LJ+1sPZFGcKHEsPF5pLX1rfoL/enEMidfV4vYHqUUNC/IaEIF/Gi5eMTChcZLgTt5OvSwbZWA1n4u2tB3DHog1CuaCGXOmtNZgUshHZrlWdfn/5joP6cxnuza790CEfWL4bSzY24CdPrkUhMYe+YazDeknMuRkKhZ0Sc3h1adpQ4hxSjOltdAOMXmh7Tow9zOWgdY86zs0cXW37WxlSjI5PY7s0aijJ0O+lQkYawJg9B6X3ihouUoaDrSq1DilWyjlc8oJrj2GYwni4sMaF8Rgvrm/A8h2HRPirYsHOVf9ITVLIMAzT31DXS+aNrtdR87bIsBRWwkGzQt4tgYiddXWx0ZPwKCDIo2CN4tWytZES4GanQOnt6uiJJu8vrQW2JHL0mD1chiQUIYXwlli7vzlvBcnf39yBrQ1tuPfNHYY8RIVQuJAgW1ohS9TwL9nk4ensieKaR1YLo6J0UDt6b9fhjNclw9cQFAJOlLcnirX73AvZZibkZ7GUF/MK2a3thw8oM8xf5n1/MRm3Mf0DXfnXKx4umdu5uRxUPvnWuUePwBfnTcqYz4t+Q3OBPE0mDxc9jGvCyMWsmJUKG3WMl9+tsAkpVmzrUq/BMxvDMK5ilzyPYfoSWhDprsZF1DaTlmPeLDPVqe7Vluj7LV1hrNzTxEpXhmEYB7Qr3h/FFrpB9Vwxe7EYFC6mcGNuJXXvNyHFFI8UCuFDU/7YQeVCAEKWqLsOdaBYFEZEa1fEUuEiQ2YV4n6p7S3f46/b32IQbmep73LEL5/biOsfX23oN9Kq2ZwXJxNbRC69HmFUlO53m+pbRUJlid06TQ1xV6oI+Ci0VDGHUyt2DimKyt7aF8jzjKopM9wr6UVmNwYXos8wjDMjxcLvffVzpc3hYn6d/N30kQPS5p1LhhSLG8b0TB4uocR4LdeV5pBicjxXw4C2JTyVK/WQYpzDxU1Y4cIwTIFyuPR1SRgmiboRKCZFQDJEnzfLrBZLLjoffncP/vvlLVhTQEtIhmGY/oLZM6SYUPO2mKcp1XrfnN/FLetnrxslOEUVcJMnATG4shRHDa/KOqxYX1iWq9ayKkOr7BQuhV3n5apwsQuxVYg6rW3uFMo0tZ+op1cFYplQY+7vb9I8UqxYvde4LrMTFqoCPtUrXFVquU26ZNBHMpTL6ckV+4THlZoXq7eUzLIpVJeF9DBiQ6pKdG8sOXRxDhemr+mt9YB6+HTN3FwOkj/IfpFJwSw/pv4n5wKaHzL9rjRgCilmGlelh0x3IufZM6v3izGGqJQeLqGAYS4qNkMgr8EKF4ZhXIXDCzFeRLWmdcuy1i2WbGrAfW/tsNzQy/6kCmO8hLrBkmWVwgNzqAyGYRgmlbbu5FhZTB6YVooUNYTF4fawrYeLW2vEYgi76QQrr4SB5UFMTShcdmfh4WJe4/SGAiacEAjJHC12lrHJuPSxwhrW5HjN5FWkMimR4Njt9kX3RK7r1GOr98pOiWWFer3p2kqKwsXmutT2aFBkudyW1Os1W2IzGk+v2i88i3786GqDh1FvzRWqgHjy0Co98bbsy/Iemo3Zil0JzhQfvZX31LD3TTM3pPaJ5G9lyDA7dOMENX9L0G9rFJCSwyVhyBM0hWqUn1O/fWlDPZ74YB8aWrTwmZUJxT2dQ527yziHS15w7TGe549LtgpraY4FWhzIhRnfL8ZLqIsvrwlmaDP1xpYDepzsvkoA6JbnUH+Jqc8wDNMbtCkeLh6bnjLSkQhFoTJ0QCJPR2dqDpeyRKgKt+bhZEjLIqs4E1aGIBRuRNZXNoYiZLmq0htVIwX0x48fhE+ePE48nz22xlaIVIgyqeu8XPU55r3LceNrCrKeUZUpallVHUs4C0MbtT/tOmitcGlo6RIPClUjPRXs6snu3G73M7WtqvlrGKSE+zHTW2Oe7FekYDkmkeR7wuDKlPCA5uJ4ba/F9H905V8W4/WDy3fj9mfXZxXC0ak3pZXXl+y3mVJWScUKXZOcLzKFE1MVKvJ6gmYPF+UYtU1dhs8qSpNKFjWsGIcUy4+k/ynDeBDS6L6/87C+WVNdphmvWxf0dUkYJonaHr2mB5BWjlYCFbn+81qZLRVZundb4jMeBBiGYTLSrgjUim3cbLfwcBlWVSY28s1KKCLpCTOgNChCZrllgdpfFPzWHi6hZNiebBQuCeWWWkcBFFaYrQuEgn5cOHMkjhlVLcIOpbPadRs3PDHU3w0oC2LikMqC9EtVoaGeU32eTUgx9dpleBg775Zpw6sSio5uRyHFVCIFVLhwDpf0UHuUuZGI3poqVA+X06cMEV5s4wZV4M2tjdrnNmMwe7gwvU0yn5Dz37yz/aDoV7RmGT+kIue9b7ryGPKeKgrMdEgPGDqGnAuk93A6zN8xhxSjfkzKHCqLuQiVipyVZK4HoRnNVLDCJS/Yw4XxNOqAxvN2cWC2cmcYLxBVNrdqclovkFRUGMtFiyFpreNVSzErKx95HV4tM8MwjJdQhWjFNm52Kgm/JdJ6vqUrrF+P/J6MEe5GDg9tjizOejNjFTa0ujxkELrk6uHSG3WjhzxJCHfGDa6wNJLTFS4FCJNqDvOSi6e92i4vnT1atw523cNFuUfq2k9V7GRjca3uufYc7rBUEG2saxF/jx0zMKMizy6Mrdt7u+5I8Xr39RbyXn7utAk4Y+rQ3s/hIkMgJQS1U4ZVCcWqFBjr+34OKcb0MbrBXxZtTyqRsxnj1aaePqSYqXykcIk58+iTH9PhpYJeeq+kw6xgMYcUoz5cmggRpq49iaqy5Jxdzh4ursEKF6Zo4Gm72BKW9XVJGCZ9rhGvkIxBb3pfKWZxhBQze7l5s8wMwzBeDRnj1bE+Gw8Xiu+vWVACLYlcXjKkWFVC4eLG/OBGzg6vYGUIQh4uFP4pW+EqJcJV6Y26kR4umSxwdSFtAcpkFS8/12PccMnROO+YEbrCy+09jTFHSvJ99T7n6uHSHY6hoVWLyW/VV4dWlertyu4+2Cl73BbyU1klHIo6vUCY+tYX503CDy+Y3rsKl6i1RX4ypBgsy+OGUp1hCp1HOJffGNceyCKkWHKOkWOwHfoYnaWHi1kpY1bAEKUBo8KFFDAfOX6Mvj5TvVqoHFbHYJzDChfG0xji8fJCrCjoL/G0mf6F0RMDniLpGZIa61Xi1f5ksPIxLVq9Vs8MwzBepJhDinVYeLiUBv1CWUA0JRQuekixhAWlG8JCQyimIp9vrHJm1FSEcgrB1Sc5XCLOLHDVvA9uC9jNbSCXNmZOaKwKvQrn4WIdniZXDxeivqUr9Tt6aCjFetriuug9u7pzu5+puWyK3UutUMh6kQqPQoblk6h9U7Yts6zXrDw1t0G+n0xvk8wnlL3yJJv2qvaPdP3QXI5cQorRMXqiUX1tlQnzd4IWSho5T5MXMvGx48fiw3NGG74jPVzKQwE9nwyTG6xwYTyNOk7Fi2Qz1dTRc0QvMjikGONFDPFWPdY/7TYrXi5zOkGBvnjlMYBhGCYrDxePDvWWRKIxg4W6hKwwSVkg18Sqh0sypFj+F2pMNl5EFWdTl2aqy0IpYXs8G1IsIRDKlNRXzdPh9hLBHJY1l31IUimRULjkENItH0VDLEcPF3MIMCuPqWQuDn8yVJ1FHYUtfluWEL6poW7Jk0pVFueCOn7wmtGapMJDKlwK6xG0fn8LvvvgSry785Dh/GYBsVnhYu4jLAdg+mw/nYO3Sq4eLun6obkc9Du9P2dQYshIYDQu9jg0aLDO4WKvcGlNKFysQobJkKBlifBjTO5wDTLFk8OlCIKK7W/qxDWPrMI/3tqBI5VkOKG+LgnDpM814gW0OOPIuFnxUJENWAkK9LCCXi00wzCMhyjWkGIdptBV6ma+xuTh0tlTgJBi/cgL3pyMnKxLqR4z5dqwotscUqwX5mLpoRPK5OGiCJncbuup4VviOd8HXbidRjGRD6r3it1az8rryQ5z+UgBU9fche2NbanX5vMpnjtWZUs97wAl1Iys19ueXY/rH1+TlWIobQ4XXjNmTFpfSCWgmuunozuCDbUtln3CyltN/E1jNMYwvYGu/HPY9tQ8cFl5xTg0iEyZk+JxJaRY+nOoxhZyvsglpJhVrhhpGCEV9TKni0pFQslulYuNyQ5WuDCeRh2mimEdVtfSJQbu2uZUV+4jBTlhcSxexkt4Nda7YdGWRljgpTJn8sJJ5qTxZpkZhmG8BAm3JIVIJl4opBLFDG3waypLxPPmjrDw3pBCWalwcSWkmEcNKXLBLOSWIdmkUNOskMkupFgveLgkzpnJw0UVMrl9z1JCiuXi4WKy5pf6IbeVATIEm/ne5hxSzOLaf/LEGtz+7AYcaOs2Xps/vSLJyttKje1Pv6E9Xm1Tl/BwURXG+YVWy/kw/RpzSC9z7hS3MVv8y+aZqnBBWiOrYh+TmeLDnEu0UGsIp961KYaUFK7Rpj+ZkZ/TIfT5NQcPl6BF/hXzcaxClcmQYlbeL0x2sMKF8TRqGLFiEOBzOC378EgM4xnFgIfaZro8LcUQLiVtSDGPlplhGMZL9CjC9mJaO9mFE1I9XA539OjhxAwhxVy4TnXOLKZ6s8Is5JYKl1ySzKeEFOsNhYtDC1zVw8Xte5a6hoq7EL4pKfQqRAg29ZziuXIi831MR4p3gfLTbQ1tBsVO0O9Pm5tGDXcmqUrkXpLHVtd3Vgoap6jX6KW1uZeQVugUCi7XMSEvhYsph4zEHO7Q3AyKfUxmio9sIyyoyu5sjBqc5piW47BUetAp9LCVmUKKKZ5sTudXK6MHGu8zK1xSlSqDKkoMaxEmd9hHiPE0ahixYpi25Vh9JK8xzBYxDOMFDBaEHmqc6fK0FEO4FGMc28R7rHRlGOYIhgyEfvvSFgytKsGVcydmlXuimMbNjoSHC1lCqt4uxhwuYf0zClsRkoIHNzxciiDPmVPCsfQeLlmFFFPCNNmFjXIbpxa4qlVvNgIuJ6SGNMpHuG1WuLhbVhmTP52VdXYeLvY5XA619xgMF0kely4slTkfjJWHi081CMqjblSFC4cUs0bWrxTQ5jIm5HI+efykos4uhwss+wh7LDFeN3x2qjgxYxyzMx+f1kSRaFS8lr+VSm87VMVqNh4uZm+VkmBuHi7Hj6/B5+ZOwLGjB2Y8J5Me9nBhPI26liiGhVhS2eD9shYKFrYyXkT1kPPSWGLlIeJEGeMVrARe5lwuDMMwRxIH23uwbl8z3txywNH31Q07DZvF4NGtKlykcsWgcCnXrCMPtnfrHi4UmsJNjwGjhwuKGrNwPalwQdZr6i4lEXm2v82VZIz59EIkn89XsDBdboQ0Mic0LpQ3gSGHi42nVjYKF/O1qqG6DneEUzwlzOGg7Mpm9kyT51LLaaWgyUU5yGtGa8w5H3rLwyWSwcPFrPiR5aE+rr7PML2Bum5yOnTmHFLMqYeLonCRr81hK+2Q+hihcElcUGkgh5BiVh4upu9YhQ2j45w9fTiGDSjNeE4mPaxwYTyNOojFi2iwP5IXGRxWjfEiXvVwSbfYKwZhkmGBqycrTLz2aqEZhmEKiBqSxYnyRPVwUX/vddp7tJBiUrkiIaH7+CEVwoKT8jxsrGvVk7C6mfDZqPAvbnNqGZZJCjGlEkuGEcpGqG1OYt4b7UkK6Z1Y4BbKQt/sMZPt8dUEylJGVaiyGnKX2BjXZJOM3nztalgwCuunnoeUSbJdWSm9rBQu5YqylMqoni+f/Z56jV4yhvISsutLwalsm4VWuJj382aLfLPiR/5OWtSzHIDpTXLJe5qrwiVdOHAVWQ7pHaYqq52HFFMMGiy8VcyY52CrHC6pXjCsEigk/bJ2//CHP2DixIkoKyvDqaeeiuXLl/d1kZgcUcfLYpi3k94dOGLRXYuLe+/L9DPSeZL0JepYYV7sGZVE3uxQhk232cPlSB4IGYY5YslWwW+WpXtpjkpHp42HC1lPkofGSRMGidf/WbXfUmibL07DehQD4UQjuGDmSMwZV4OTJw4Wr9MlN7ejS8mZ0xuCz7ga8sSBBa7eBtzO4ZLGSzjb9iTLKGXMbrcvValhFBRafycT5mtVFRkypFgylI1yXZYeLqnvUdhAVcCuChnzCQ1nCClWJONebyPX//Ke6WNCrHc8XOxCipnHctmWpGKI7yfTm+QSYjR3Dxfr56nH1/6GEgoNzQhHe8/C8cSAGs5SjudOcriYv2P1G7OCpSzUL1UCnqHf1e5DDz2Eq6++GjfddBM++OADzJkzBxdeeCEaGhr6umhMvmGAimDiTuZw8X5ZCwWHVWO8iFcT0KtxtlPjH3tfmGS1WDX/ZRiGOZKIKgJLJ0Jls0Ldi/r1RWtq8ZfXtxnWt+3dET2/g2r9LDf45x0zwqCYKS8JuipsN8zrRb7mlGuBmaOr8d1zp2FIVanRmj2rHC696+GiCpGcWMomk227X450rzOhKg5kGWW71rxf3KtH1QPFzjK7JwuvpnQhxcwKFxKIpwvtZ+VNRd5pgYCNh0s+ChdFOeh2Tp/+grxv5rxChdoXmPfxdh4u5nCHsh3IMYD3AExvos4njj1cLKI0ODqXw9/JPhDS5xHrfGZWyP5G/SobgwbyMFadZ8yKUnEcs4eLg+MyudPvavc3v/kNvvKVr+ALX/gCjjnmGNxzzz2oqKjAvffe29dFY3LAqEH2/sRtXpwcicgNyZFcB4z38GoCeqMiyPiZWkyvCpOMC1ztL+dxYhjmSCZbS0uzgNOL4/1za+uwbPsh1LV06e/J3CwVpUHDhl1ac04eWompI6r090cPLNMF2bkKr8l7Q+Z9UOup2A2ddMGMKfyHtBbPJk2GmheDKLTcU1UehPrQwyUlLGuWx1e/rwu3FcmVm/WoepGo5VbHgmxCiqV4uCj3pKUzLNpEUnCuKr1SL0r9rUHhonhWFMTDhQX0lsjboXtdFUgJKDF7tuieUSa5rTlXi27NnxjDeA/AeH2fb9iDRwsYUsxiXsyUw0WOt3T4bEJ2Ur9U52FLD5dAMmcLjStW5WPcI5kBrR/Q09OD999/H9dff73+nt/vx3nnnYelS5da/qa7u1s8JC0tLeJvOBwWD0ZD1kVv10lPOIxYXBtkwuGI5+9JTzgiykuxmL1e1kIRjtKiPiaS7lCflAsyhunLcaanJzmW9PR4Zyzp7ulJlss073SHk59FIt4ps0q3MkZT+anPR6KasKcnHPVkmZkjm75azzBHDl3dybGbnod86TfytGaMK5kKaRwN++OeXN92dvcgHNa2jy0d2nWW+OMgGZu8ZsRo7NfK/60Fk7D7UIcQEpICZs/hDvG9nkj28wPV0w1PrkNpKIBbPnQ0upV5vdtje4RsxxlSJGlrZ+P+IRrV6j2cRX1192i/Udc/hayb9k7tPvjgQzwaQTiWft3vE4mDY2L9I9uSG8h+RAImElB1Z3ndar3J64jJPU2iL7sV676zW107JdtuJHG/xfmyKL/sn1bHJ+oOtyMciYn34rRGi2vP1XNLuhLr0opQEB1hzYst6IvT3U3ctzAifl+y7+XRvjqVOqf9o5f6sBcghUo0pq2pY9Qmwz5EI2ob0byX3Kw3GmvUMUe+Jum0eh5qR6rMQ+7/5Vxg1bYYplCo+3yyOXDS9uRYJ36fRXs1yCfTzM1yXPYnxk4V0Z9hr1SPxeTcHwH1cu04zuSLqesx428C0MZ/ojwQzHhM3jdZ47Q++pXC5cCBA4hGoxgxQnNhl9DrjRs3Wv7mjjvuwM0335zy/uLFi4VnDGPkxRdf7NXzHe4GGhq0xe0rr9ZhaBk8zZpDPjQ0+NAWBBYt2o0jkZ07/WhOjD/PLnouxSKGYfpinNnWkhxLlr9Xj+4d3hBmHexKluu99+sR2ZksV31n8rM13fVYdHAtvMaO1mQZ3/+gHtFdcf31ho56LGrb0MclZBhvrGeYI4f97clx8fkXXkSlMcWJAbJerE98V5LpN33B/lra6gMvv1KH4eXae6t3+9HQAaxdUY/6Rh/aNNksXnrheUNIC8kW2qsl5rxc1skdEWDzLq2unlm0Ew3KHLmuqx6LDq9DsY4zu/f40RUF3nitDuu0aGKCtrB2jSRAWbRon6NjUR1R3UiWvF6HLZUoGC09Whkpn+9zzz2X8fv79/vRHjG2pXyJK/2o1A90x4BXl9RhQ0V27Yuug5quvA5ywJBt7Lnnn4dboe5X1vrQ0Kx1kvcSaydiU6JPERvaGrCoc5Oj461o8KHhULLTremsR0Nr8vVTL9Shto7EfsArL7+EdbRfPeTDikg9SmtXGo61qUnby1aHgJbEfm7523WoTdw32o/TkWW9vP5mHXYlHdmyYr1yvd1N1MZ35nagfgoZ3ct6funFF1EaIA+k5HsvvlgH0gG6uZ7ZuMePhnYg3tKARYu2Yt1+HxpafFi5ItlO1f1LW0Aby3fs8ONwD/0OaOwCVkbqUVa7yrVyMUw62hNzJUH/L1q0KONv5HqEePf9eoSVPXg69rQlf+drpX5Cq5tUNhzWxtJQW4M+zkleeP75tDKyrc3aOei3YhzoBN5fXofD1iJtAw112lhNvP1WHbaZ5tkNiTGeqBRrsb2ZD8r7phQ6Okw39UhQuOQCecNQzhfVw2XcuHG44IILUF1d3adl85oGjzrZ+eefj1Co93aB+5o68Vq7JrA786wZmDDY20qw4Pp6bPtgHwaUhrBw4SwcibxJ1odtmtfYRRcdx26KjCfGmWU7DmHNW9pG7rjjRuPiY0fCC+w62IE3OrTV05w5o7FwVrJcW+rbsLRzs3g+Y/IQLDx9ArzGe7sOY9UbO8Tz2bNG4YKZI/D0YW3zPnXMQCw8e0ofl5BhvLGeYY4c1te2YPnLW8Xzc86dqefjsILCBskxU3LOecdiSGUJvGRl/eShFeL5vPlHYepwTbq68rlNCB9sx9nzp+DgB/tEuDEKgXXJJcelXde/+cyGnNbJlIticYtmeHDBBXOw42AHlr2kCTqOmjgIC+dNQrGOM88/uApdkSguOG8mhg9ItpemjjBeal0jwo8sXHi8o3Ovem4TYgfb9ddnzJuKY0YVbk+7v6kTr7RtELl8Fi6c7WifcKCtGwsWTMfkYe5ogsij5alEP6opD6GpM4x586fhqBEDsm5f1IYXLtTaMFnvP9OkHff8C2ajosQd0UndWztxaMch8XzO7FFYOHuUeL7hxS0I17eK5+OHVWHhhUc5Ol7be3tRuzGZr3bS6Gq079eidhDTZo/FirAmVLvowtnwr69H3bp6HDtjOBaeNNZwrIrNjdiwfA8mDanEjkQ7uuj8Y7Dp5W042N6NefOnizA0cu162tzJOH5cTU71sPr5TQgf0M4xrKoUCxfOzOk4/RUKufafRLteeNEc4d1Hc8aiZu29s885Bm8secXV9cyml7aiu64F4wZVYOHCGdjz+g607D6Mk04ai3NnDDf0+zeeSfb7pU+vR6ilC1OHVcHX2GbZthimUND4/WJr0jDx4ouPzxhlZefBdrzZoSm1jzPtwdOxam8z3l+yTTyfNLQSCy+abvm98k2N2PTuHswYMxDhfc2Gzy5ZmL58tL9e+8YOTB4xQIwDNKcvmDcFs8cOzFg+6ov1ifCv55ydKj8dsuMQNiVkIqMGlmHhwmNsj+Xf9leES0fhhdV+3jeZkJGxjiiFy9ChQxEIBFBfX294n16PHGndgUpLS8XDDDUmblDo83oJBMLw+zSBfSAQ9Pw98fsDWnkpfqLHy1ooyOlcv2fBkB7Lm2H6cpzxyb5J+AOe6Z++gFIun99QLuNn3hxT1Hr1USLWYND2ehjGS/A6jykYPmVczLB2jSKa/K7PJ5Qb/oB35igpdE5eT7JsXRHt/YGVpSgNaWN/SSh92UtLIjmvk31+pa4CQeP84/NWnWU7zpAFK11LeWmJ4fu0RZXXGAwGHYXppeBe+jws9iaF3T/FfD3ifNQGnJwnmFjbuNnO1X5UVhKEvysKX5bX7Q9o7ZkMxeTvgsG4fly/i/vQSDx5j6gdy+PGfcn3qU04PZ9hjWs6PtHUlayf0hLamyXWav7UdVocfvFZdUUJ/Ic0V6nqijKxn5P3zee3Ln/W9RBLtm/aP3qxD/cl4XhivBRjQYkIl+fzxwxt0u31jGyD8n7Q2l70b2o36thUEjV+L/E70f9s2hbDFAo5fkuCwZCe78iZbMB5e1V/R+Ol3e/k9+T6SC+r34eSkvRGNSWJ31B/DMe0eaiizLg+sKNMOV+FaU1B0DpDfl5u6tcGDiwHVnwPgXgER4c+jlDwYu7TCk7rol9JQqnhnnjiiXj55Zf192KxmHg9d+7cPi0bkxtqzqtiSL7m5WTRja3dIkZ0oVGv3Yv1wByZGNplnok5KVkeWXa5kaxSLYu5v6ivLXKYegJj+U2JBLn/MwxzhCdvNSfyNqMmnC6hmEymRK5eIGKT1Lu9W1tTktV/acK4piSDV7OeeDuH+cFQDkrcbahnj1Va1nkaZHJdo4AoqAiMMrUlu++5nZzeTDYJfQnZRPJJtm5GbQvkoWJ+L5tjqEI6EiJLHZebTUzWmbke1DWV+p1MyPYvy0peECrdyv6PvFNkAnar9XA40ccHlIUwtKoUQ6pKhBeD/A21J3UcUJ9nC1lu69fAa8YU1NsjxwJ5H7TP3a8z2Sbk/ZDjiRy7JYluppdBH8Nk/3OxfzNMJsx9wcl4og6xTudX87nS9UF5zJBpXjf3JSvIq1UcPxZHOBLPao5Vz6euISRyvWZ+rtO4FFh5HbD4VCCuxSajHF6WsWKZI8vDhaDwYFdddRVOOukknHLKKfjd736H9vZ2fOELX+jrojE5oA5ixbAOk2O11wSN5GZ5/eOrMX3kAFx74YyCniudAJlh+gp1IZXNosqKJ1fsw6I1tfjm2VNx4oRBeR3LLEBSUTf3Xu1L6oKW+n42gkaGYZgjfb5RPydBVbdIZhr3/PWQkqCjRypcAsLy2kqwYEYKs6M5CGnN5TC+RtEiBdxESEoxTUIXguZXJxv3lLVEgduTFO5nUraZhU1ulkttC7ItZrsGsRUs+3yi7t1UCKjKFLPhisSsNHFSdhLIdYdj6Kas0TaKDbo+XXliqXBJKtBu/sjMpJJGKktj5NOQJJ/7qJaLBfSpyHGSql56t0klIFV7IcY9OX7Ic+v9wiS41ftxogyyLKGE4QDvAZjexNzenIxLEWWjnZXCxaGsS34W8Pv1PkuYpvn0Cpe4MiY7nGNVxYxVaH/189JgwPjhgXeAlxboihZRhiFzsaHzs5jo6OxMv1e4fOpTn0JjYyN+9rOfoa6uDscddxyef/55jBgxoq+LxuSAcQjz/sQtB2CvGdpRrGQa5MnLpdCo8xWvtRivYBDM5Llpbkj0Izf6UzrPG7MyoxjqVS0nWysyDHMkYrD+dqhwIWGWtET0mqDKyjCgKxzTvTzJwyWpcHHm4ZKLkNboaWNUTHlNSZUNqtDH7OESyMnDJabfCxLUFHr90JMQBmVStklI+OR2O5fHouYl6zDbNYidYFkoXGD0qMoXVZliZ6iiKuIyIb9K95wULmZljfqark8K86xOIftZScCHslAgRVmqNS/nY1w6VMVQHo4y/RbZNlTFq3wt1twFGPdkGzRH7TCXQfV4Ur8nhcJ8P5neJCVKhANZnPqdbOYjp8aFusLFl+yzVn3JCn2MjsXRnaUXaUkgYLum0D5XFC4h5Zjdh4A3P2VQtqBkMKKn/RPxJcn8OMwRrnAhvv3tb4sHU/wUQ0gdq/J6beMnJ4Pe2MMbhMQeqwfmyMUQ6irPjkCCHvHXBc1qOkWQm0qi3hmjjRbHXlUSMQzDFJJsQl3JeUQIQaVAswg8XDp6IvpmnoQAUhCQSeGSzzWqdUlCXnVf4LU6ywZVYG0O/6G+djql6gLzoKZwKbQCLxzJLaSYm+WShyIhlRqKJbtj2Fjy+32giFxurmlUZYpdKFapyHKCLJsUpKWEFEu8pv6n5drQrtEqNK60pjb3ZakspTFLzSWUa73QudVy8poxFVvvEr9PfObWuEfj+b1v7sDcKUOSChfT3xRFpBKWzhAWMfG+G2GXGcYp5qWWo5Bi6loti/bqNOWBHNKkkpsU9/J1JqQXDPUjOcdmWl9JpJeZeO7P5OGinwh45wtAx+5EocuBE38HjDwfKB0LgBUuudKvcrgw/Q91so4Xg4dLooheWzMmFS6FL5jBzdJrFeEx3tp6ANc+sgp7DnX0dVGOLAFYnv1Ad7d3oX2nU1C4mXemUJgFXkarn74pUzHzysZ6/OjRVWho6errojAM44IAPdM4KIUE2oZc/sZb471qXCCfJ8OJJZI2J6woMwndpaCBpopsBXLGejUq+N3MB9JXHlFUN6og25xDxGkYNjW8lHhd6JBiergTU2gSGwqhWFRz4OSq0JFtyCwLS5YXrqEqU8yGKxIpZHOCvMeU2N58fNWTRCpNnIQUM4ei0fO+COG6dQ6abJVOahPw2rjnBTIpO9yqsw21LVixuwmL19en5G6Rr82yXtVKn74SM7VBvp9Mb2Ke55zML+o4ls0+22nYWHlM6q9qH3aSw0V+P6ysdZx6kaoeLFa/sQwptum/gH1PJ94cAnxoMzD1q0DVJEfnZOxhhQvjadSxshgMJeTA6jWrDrkY7g2hrdFSq+CnK2pW7mkS+XU21bX2dVH6PQbFQL4eLokFWj6JQvWyxNPlcPG+h4t50VkMeWe8zAe7mnCwrQdbGtr6uigMw+SIURjpzMOFrIJVgab3PVyS+VuIpIdLhhwuysfZCmrNodqKwSjBCXYeBZJk+Kd41h4uRKGbk15+xao2HclcIO6VQbZLqiv9+FleuNy7yZBnEtmko4UKKWbj4aKtqbJUstl4uMjXcozRlVKWHi7yWHZC/tyTTRvKZGoAXhv3vICt15WS38ENehJJuWmMNedu0YXG5pBipvxSusJFDynG95Ppy5Bimduf+pVs1iPGaC7O8oLJ8ZNQn9sh+5c6ljv1cFEVKlbeNOrnZRRS7MByYOWPkl847X+BCvJqYdyAFS6MpzHmA/H+xK2W0UtKF7n57439aH+Jqd0bSOFBMVtmFgvq4ijf+pb3zR0Pl+Rz8+GcLuj6EmN/N9czu7hkSzhRZ2pCXYZhigtVMZBpnpDDJG3AkzG74fmcNO2JkGK6wiUhCAhmyAZrSAKf5cRmVPz0nxwuSYWLtRAmnTeCFVJgqudSKHQOl4Sw1mlC32yvJ1vBtDHXiHNk206x5FdCJ7mFOsens5Z2GlZMlk2GhzF3B3kcWffpwq7pHi6mvqzmmMo12bRVeF71OF7aO3sBVWCr4rY3ZNKQLJbq4aJ4YRrKoDQP0SZM404xK8GZ4iM1SoS7azW7c6Vr52rOFrX7ONC36N6u5vxbTtDXY4FUr1n1c6ISTcBbnwJiYe2No68Fxix0dB7GGaxwYTyNGkasGNZg6pjrJVdauVgqtLUJLZSdxrVkkveDBdOFx7A4yrNZyk15pMAhxZy6LPcl5vK7Wc9HIlJQ5ob3FMMwfYNdiCCnHi5eG+8NobwSY1NHtzmkmN9RSDFjTpIsPVxMYYyyCd3mZeR4bw7hJMm2Xch6kgqcXgsp5jCHiy7sL0BIsUAeHi72wm33+6W9wgU5KVxkX7BKkEx0hc0eLvbKXT1fgOl+qvfNkGw6x/totYbmbaON55ZdSDGXKkyOQWr4Ij06RgYvG/kd+T3ZBot5TGaKj3R5UO3I1WjDaY5VQ0gxpb84UZzI76gKF3OONzvkXGy3ppCK+VJ04LTdVwHtO7UPhs4F5tzu6ByMc1jhwngadUFXDIswr1qky81XoS2HzJNbMdwzN3hjSyPueG4DWrsS1gFZeh6xcLXwmC1j3TiWG4qydCHFikHhYs6NYyWYY/pGmccwTCq7Drbj9mfXFzSUp9p/M/VlNUZ/IQTRbmCVK4WSLBOVpQGDcDZTSDFV0JDtMGfwtIkaFfz5zut9iVlBYiabUHNaAmuYQooVtj1lm9C3EIpFVTCdq0eK6m2mkqsCx2myeOPe0XgOp3lc5O/s8ujoOVxMHi6WIcVkvgCLRO3WHi659T3Zn1X9FoehMqLnTzEpAaXg1S0vEn3tSR4uSnh0esh7nRJSTGkf9Ht56zikGNMXmIchJ/NlrkYbTg2s5UfUVQwhxZzkcEl8RyrnrXK82SH7oHkMl9BxygJRfDP4Ywzs+EB7s2wkcMaDgD/k6ByMc1jhwhSNh4vXNqBWqJsaL5U3GYe1sOcxzzleqoNC8vrmRmytb8Pm+uwEOBxSrPdIZ0GYLXLx44aizC52N1EMyURTcriYFDBMjm2riIWHDONl3t91GNsb27F856GCnSMbZXkyjJF3PVzS5XApT3i4jB1UnvhbkfZYhiTw2YYUM3kOObUy9ToyZ4ad1Wsy50l2AiQpfC+0pbnqpdVnChdFMJ3r8e2E21JQ5pbiShVOE0bFoUnhEs0yh4uNl5Gew8XnPKSY2cNFrVejF5+jItqeRy2z18a+vkZXyJuUsW73IRnejdpm6rreeE6rsVzdD3FIMaYviOXi4ZKj0YZTzxh9ThEGNcn3ncyVZt2KnfdiLh4uCLfhy4Ebcax/mfY6NBA4+wWgcrzjczDO0VbJDONR1DGsGKbtdMLTvkSPw1rgMuUy2fU1LV1hbG1ow5yxNY5jY9ptcJ1ujCTJ+LgsXC00mRZH+5s6RVuYMbI6i/vmrsLFfDz1tVfjWptDinl1DCwWdCUsewcxTEHQQ6c4tBwvtMLFEAopC0+G3sQYyitmULhUJnK4nDB+EO7+5BwMLM9sHUnCXlqP5pPDhdZd6s+LYLmZOaSYTf4bKfB3Ypyj1pEUuhQ8h0ui/E49XAoRoiumCKaTCcVza18puSp0BaE7ZTXnaDPm8jOWWfWESYfc35kVLnQtWm4UY1vSwz5Z1JEUvpuFgup9MypccvRwkUqigB/diZBnXhv7+hq7MHfS0t2t+pJeTTQWpYyzNv1CHcvVNp2ubTFMoUi3h3bym+w8XNS9b5rvSc9LZX1HOPFUSQnhlyE/nopUelp6zTatBV77EI6HFkYs5i+D/8xngEGzHR+fyQ72cGE8TbHlA/Hq5k9u5gottE1noe9VHnlvL/7wylas3NOU8zHkhJqtkJTDB/UeRrfh1Pr+71e24NcvbEJTR0+f5XBJWSwWgbeIUZFVHGHQvAyPCQxTWMIylGcBDR2sksxnFvL6iyKkWNQUUkzmcCFqKkocCRJyvU5zgtt0OdCKsU3aeSdko0BQ27UUthS6PekCeocWuFIv42a5ZD9TQ7dkuwaR5UkJKeayN4FZiZI2pJjDcUq2/xLTPSgLBSzrXl6itYeLtQJNKmCovOnC4TpFKmpKg8ky8rrRmRIwOSa4dZ7kvKjeT7rPyb5lrXAxt2nZbop5TGaKj1xkUAbZQI45XNJ6uCj9V10bOTHwNfc3px6kBg8Xs5KmsxZYcrGes6U7Xoa62f8Ehs9zfGwme1jhwngadRDzqoW3ijFhtHfKKycR+lPIejQvlL1UB3Y0JwTs5N2Qr2WQ041RSi4Qtmbv8yTGzZ1h0T9auzQhkhPhghueSek22kZXZ3gStVzmTThvnLNHCstkG2MYpkAeLgWcdw3jeiaFi5JoWMo3vdb9rXLSSA+XioSHSzbI8DjZCuSM5YgZ6rmYldRJDxebkGJ6EmrnFrsk3NHzPPRS/kY7Dx0z0lLXVQ8X2Y+E4jJRrlw9XMwhxVxWhPakeLio+VCMnipO9wfykGalnfm1vEfprkkPKWZSuKi5cTIZMamf/fOdXXhq5T57xU4wGZqKHf6N2Casd1kJKO8F/VXlBJonoXW/UMuhtofeUvQyTNooK05ynsULG1JMV+L7jCHFnClcjK+zicKSDCmmJsjqBl6/DOjYK152Vs3Gq5NfxMgZH3d8XCY3OKQYU0QKF/eO++j7e7F2XzOuu3hGivWPa+WNedc6MZs4kPklLIPnkcqSfBJ8x3I8Bluz9x6ZrFGSQrhYr963dLG7sxHa9RXmMnJIsfzQ26FH7zfDFDtu5uAiSDjVHYkZ1pLq3JBpTlEtIAsRaqlQHi52Qlkn5JqE3CyY9qqRU+4eIn7XcrgEezEnUNIjwtneIleFSDrkNfoNiqbcjmEn3M6njVF/+eVzGzFlWBXOnD7M8JlaTjlclAb9wmvAqSee7Bvm/kjHsbqWZNuwLqt2LLNXReJcpnCA6doX5bhcsrFBPJ86vAozRw+0DKWnhxks4n5cCJLtGgVVAiZzvaYafqnh+sxIJZwhpJiuUHWlaAzjCPMw5KRv5BpSTP2uNGa28u5NKlyMCksrbzEzZk9Lp/MrMayqVPs7oBSIRYBD7wGrbgAOLte+UDEe5ecvxkXlIxwfk8kd9nBhPI06VLq5BHtuTS32HOrAuy4nTTUsmj20aDQKQQt4HrOFvofqINNGN5+yJgXw2Xq4sDV7b5Fuc0gLpaQQyYFAI+piDpc0HiFeHU9sFUZZbMKZTMJgHhMYphDIMd6tkGIPLN+D7z6wQuQBk0SV/ptpbSHnE9qMezWkmFWINFmPJcHsDXikHCHXMKzyt8Y46qnHorBnuw62w+tIBXvIzsMlC8WJmmQ7mewdfaowMqOHpnJxjZBUlhg9MbJBtUa2zuGSe3lrm7qw80A73tl+MDWkWOK4tBaV3gVZe7jY5HBRw3WpbSndWCPPaVbeqO3QyVqvvTuCJ1YkPVseXL7HlB8kqdjxav6qvkYNOamS9IZ0p77s1pw05spx19LDxWdcu5LQme8l0xek7KGzmC+zba/mY9v1Q9mthEGNMr87mSrN/S0bD5eJQyvx8w/PxBdPnwi89Wlg8Vyg/lXtQ38JsOAJgJUtvQYrXBhPEy8CC+9cXAx7m96yOk+Nn+mdOrBDCg3yWbRmI6xXYQ+X3iNdqCv1vjnzcJGxjuMF7ZvFYL1riPWckki1jwpVpKiKP1ZWMUxhkIIlt0KK7TzYLvorGfFI1ENnmifUkDFeFVQZhKt6/cnE2tlvJXNVBJiTORvjr6d+/+9v7MAt/1mP3QeT98aLhCMZPFz8OXq49JLHVCaFkRl/Adq5mn9FXne2azS97kyWxG6UV80dZV5nWs37UlHi3MPFmCxZP07IFBYsUTfp2pQMeZaqcEmGgnMSUuyNLQeE0mXEwDJUlgaFUnrV3iaL+vbnrCTr76iKRJWkwsyd89jNh+q63qp7Jz1ckuXsLc86hkmrBMnawyXuevgyq3kpVw+XbNda4wZXoGzHPcCex4wfnPAbYPAJWR2LyQ9WuDCeRh373Jq3VSVANtpiJ3hVQGpOgFcoUjX+8DxyM5OPB4Gei4VzuHgWtS2mLJQs4tOna+Py5254IaRTUBRDQmCDUlwoDIyfFYPS1Suo97uQ+SUY5kgm6aHhzgJFWqurfdYqJ0Om8qgKF68JqtS1jW4o0gchxSKmOjaG5Ey9nwfausXfxrYueBlZp3YhQ/QE504ESFGLEHUFnod15ZvDtpBUBLlXBt2SWPUUy9bDRQlL5naCcllHPZG4rYeLenwZCszpWkCWPSWHS4rSRPubrm0kPVysPX3oVDEH6+bmTi1H5nHjajB95ADtvY5kvsyk0lZRDvKa0YCsj5S8Qi7PFXbHETlcFCt9M7IdyXvZm+MOw6SNshLLL8qEG+fSw/HRvOTPUuFi+opdjjdbDq8GVlyTfD3qQuCMh4Bp38zuOEzecA4XxtOo41ncpaBixsRu7uoc1QHYS+sMoyKocOcpzpBifefhkrRqKwLNVJGjtkXz5lBaHornGSQAVsmDXUs6b2oHhoWgR7uSOf5tSh6aAuaM6m8Yw+XwmMAwxRC2Tyoj1LnDaUJpw4ZcEVR5be1kmcMlg5IgHXJOyHbdZTaOMBhlWdzOHiURtJeRbchOoJJNknn1WL3lNZAMKZalh4uL5ZLXrYZuybYf2XkTuKEIDUfiSs6nqOEzWU61vNIzJeuQYqbCm/OUyrYkv2a+B1Q+OwVash4ot0xmgyDZ/6hMcpxQx0lZn1oOF+09NkAzklR2mO6Fy3OFug9SUb2ZrBQushzSS4/mMNls2OCK6U1Sw3Jnbn9GxXHMdW8aXWGqrO/k60yk5BLLZq0V6QDe/gwQ04xOMP17wIm/c/57xlXYw4XxNOpkTU+bO8MplkHZQslNs9Ew5+6RcwR6uJgO7aU66I2QYtkegxNk9x7qJs68UFI/y6xwcW65nEsOFLvPvOrhYh5bUsKiebPYnsROYMswTAE8XFyLfZ/qMSO9DNTzZSqPEJDruSLgKawMDTKFwUqHTC6b7RrRrMgyGiWkHkveE7e8mQqF3NeUmPJt5JKvQc35oAvV472zjg45DHlSCG8GeSja1+k5YuJu5XDJX0Ekw3QRbd1RQz+IpAkpZicId+rhIj1lzHWveyGY18MGo0TrHAJ0KemMmPT3FS84aeCoKj/l2BnsxXxDxYbcc5jls257uNgpuuj4UhZjDnGkvifbt+ap6VxBzDBuYR47sgnBSWTTXJ3Ku+S4TMOtulRyonAxz0NO51fh2fLiPKB5vfa6Zg5w3K+c/ZYpCKxwYTyNOqA1dYRx7SOr8N+vbMnrmN3hpGWRy/qWXvMkyRbVcr6QglvzsWPFFFIsj3rRc3pkuamXm0FelBYeQ+gu08LIEC4lg3Wd2x4uRgGS8TP18F5tI8aQLsZEquJz3j07xiqZLMMw3vZwkYImVaCaTZiKaBGEFLP0cNGFqdkvpHPNLWKeq2U+mWS4z3hRKlySHgW+vBUURgVe74T20b1qHLYF2c7dNCwwXHeOHjRqgmMVN+pRHW86uiPib3mJplSR7VY9vlSUOPX4SIalS5/DRd4jmQ/AfE1qX0nN4ZKsB4MRk029SEUijRFJhUuqRzmHFHOQ48us/HI5h4tdX1TvlzmsmXgv0URke6DvFCJkIMMUxMNFXatl4V2Xci6b/qOGBFQVKM5CilkrvNPSvEFTthxekfhRBXDGA0CgNPNvmYLBChfG06hhxBpbu8QAV9vc5ZqHi9ubWnXg9pJFeq4a/GwpRmFrviHFtDwVyDpsBf1OthF2oS886XKlZOXhYrDOy383oW4uU3MgWQvwvIRBKUQ5XFKUWd4stxexsvxkGMZdZN9yq49ZHS+bvGDRIggpZhUiTXoImUMYOSFXxZK5js230Hw4NW+Gl1FDL1mRjcW4VXsqdGgfeU6nyrekN4N75dLzryieYtmuP9TwL7l6GDmZ39t7NMO/MpNSJWkN7dM9x5yuM+WYkerhYvSakm1CyvLM6061nEE7xVM05szDRbaLoF9v26piWleSBfw5hxns76i5iVSS3mtuzWOxjDKTtCHFEr+n/qe3LY/NY0z/JjXCgvP5UjzPor06jeYg39fmJVXhAvdzuPQ0Aa9/BIi0aq9rZgHnvw4MPDrzyZiCwjlcGE+jjmdyYZbvBF5IhYtXc7hYWScW5DxFmMNFLhJzrRajx0Msx3vCZkCFJl28aaPCJe54UxIpcEgxtY1I610ZgsIrqG2Xyme2ECqGMcDLiakZhnEXPeeKW6FYEsezEiQ6Mb5Rc0/I4dJr46Y6zuv1l0dIsVwVS2Yhb9xizlSFgnI+97rHoKxLs7Bckgw1F8/C06H3PKZkPUuviUxIwZGbhgUGT7Ecrf91pY1NgvJ8uqVqzNPRo3i4tCfXfrK81KX0nCcOLyKp9DLeA3Obkh/LtmE+vBoGzLzeTHq4OBvj9FB5ikLFYLSktNVcwwz2d9R2XciQYnbHUec1K6t8ed9kO6ViedVTk+nfmMcOJ7pq8z7bKVb5SjPNKfnmcEm71opFgbc+C7RuSYYRu+AtIFiZ8TxM4WEPF6bXoTwsNz21Fi+tr8/SKt1oAZQrag6YfBcDm+pa8dMn12BDbYt4rY71XrJIV6+zkJZu5mN7fa0lBMR67OTcNuTZWLKqqN/lHC6FJ51iwyjozpTDxV3lZbrFnlkW4aEhxTZ0TsqClz01HOO29xTDMKnoQni3QoolvCfs8oRl5eHiUUGVWUiqrp2chpFyw2PA4AUYi6X1qhbzUeLzfHM/FppkeDYbD5dEHTuz2E1amtvl6ejtkGhmCuHJpYZu0ZUJWYesi6f37MjLwyXZBtsTOVxkQntZTnUskMorp+OU/G1pphwuieOq4cGM5bTv12ouGyd7H7VdS8VPTyQZ1lteG11rrmEGj1SFi65UdKm+7IzNpDLYqgzqe/J7amhMVp4xvUlqWO54wWQ45q/ancvocYqsQoqRMlP9WloPlzU/A2qf056XDgEWPMnKFg/BChem19na0Iq9hzuxfOehjN+1UmDkK7/rUnK45LuwW7WnCbVNXeKv+XheWmgYhMQFLJd5sit0GIN8MW7e4/l7uOQY/5OFq4UnnWLDoPzKcC9yXZzZkS4sQ4r1rgf7kyGkmCmRqlfL7FVYCcswhSfiYl4PNaGwQZCYhQerviE3xL6Pe3r+VNdOuYQU03NsZDk/GMJsxuIpc6Z6PPX+ej1Eo5rrwgrZLpxch56IXBHwFHo/Is9ppzDKx2Mnt5BiueUDsfNwcSO/iNpnpIeLVLjIj+R9ovNn6wUkf2u+B2pOG+1aUpUnxnImvVLMBG1yuNjdRzXPk65AstgziRwuHlU29zX2Ye7czXljZ2xm9HCBvcJFhhRTLPm9JAdh+j8pUVachOBMk981O28aBzlclA6UrXFC2t/sfRpY9wvtuS8AnPEQUDXR0fGZ3oEVLkyvI8ckJ4sqq2RWeXu4qIlN8zyWXDhK4ZhaXi8pG9Q6K+RaNtXaEJ5GXWDm2hZyVZy4nXydSY+6kIrkE1JMFaS5IMRJp6R16rLcl5jLnyIA82CZvYohXB0rYRmmIMj1Gg23+Y6pBqG+jfdjpq5sZQHptWHTaJRA43zMeVxxC3JNqmyu43TGE+pav9vj42mmfDh24Z+s0D2PyGtAF2KjTz10zOj5Ogri4ZJ7SCM9QblN+KZ89nXGkGLSw0Xm5okZyisULjIEl4NrUHNJpoYQSypvxOvEPbKro3TeSupvjGtq6wYm19Oah4svJax3MocLC+mzVQLKUF5u7Qvs2plUBmvW9vZeT3IMUz1cvLhnYfovuRgpqvv4QoQUk0UgfbM6rzgND25QuFittdq2A0uvTL4+7lfAyHMdHZvpPVjhwvQ6yRBOThaRyu/0GLf5TeDdYTcVLjGDcMwQtshD+7ts4on3dsIyz9RLjmWN5miVblTUeLue+gNGpaNZSOPcEtaYw8UFC2mlLJm8Q7zYn8z5ocxdgD1cetfjjmGY9KghUvL1crET8BvnlAweLnFF6JhjKKTeHufDiTBqJAtwEovcTK4COcN6K5o63xhCuRVRiEZdYWGTwyWb+lLDYvWGEDuX8HJ23hX5oCsuA/6c+5GsuxSFi+6R43IOFz2kmDx+8vwyXr+TdabaLswKF1XxJo6dqHs77ydVSWJGrVejt5l1ueSYSMeSxzOGJ0zmgco1zGB/J6lANXtdaX/d6tp2Y6SugLMZ5+V9k/Oq5uEiy+5O2RjGCbkYKZrDYud6Lrt+qCrR5dhLqM/TYRi7zTnSol3AG58Aws3a63EfB2Zc7fQSmF4k6ORLV1/t/Ob95je/yac8zBGAXNw52QDEkToQ5rsY61bCPuQrDJQLUz2/TNybwtHeCnWWonDx+MLZ6BLfu14ybnjXMO4oNlRBdyYBnCpgo8NQG1fdhLMul0EolL7/eLGZmMeWlDw0Xiy0R2ElLMMUFlU47IZiUxVSSSVEioIiw5orKWT1IyYt3T20frQKSxuO2SfWdkKuigBzqLZUowRYzuVuhI/rnZBiGRQuWcSkF6G1ekGBp94Tp95OhfC8sQzNl21IMRnSqwDhm9R1Zpsph4usQ6uQYpm8rtXfW+af8Rvfk00s6bWjjYt68vM03kp6aDuhcEHGsIkRJaSYPJ6xXyrKQc77kVFga9Um6V44EuZlwG7N2ZN43yzrTfFwSdxXNReZ5nmVbFsMU0jM05yTocRoKO28vTr1plFDAqqHdRqJNW0Ol/e+AxxeoT0fMA047V7jDxjP4GiMXrEicTMTfPDBB4hEIpg+fbp4vXnzZgQCAZx44omFKSXTrzBb8jj5rvr9vD1cFOvGfIWBcpGpK17SCHX7EmO5CnieIhAQ2wlLMsVZt0P9WTabeqPQx9vCgP6AemvMGwuj1WwGhYvpt7TZKclD4ZJOSWsuiheVF2qZrUOK9UGhihQpxCR4TGAY90kNJ5lfP7PzajF7hDiZU0iYGfNoKBZ1fUR1lgw7lFughFyTKpuVZeZ1m/q5VGI4FVr3JWqui3w9QlSLdD2xdgEvX10TyTwdmXAjJ4oZeY3UJGUxslU0qUobtz1y1PGh0+ThIgXTSeVrUrjmZIxS+5EM6aSGllP7qbSSVoV39F3pnZSuLer1Gnfo4aIoEq0ULvLaqCxBRBFA2PP7Rq/kcHFbQZUppJhdkm+zwoWKpfYfOqxDxzeGyYtcDP6sjDactFenESikYkZ4uCh92K4/mVF/Y/Ag3X4fsO1viS+VA/MfA0LVjo7JeFTh8uqrrxo8WAYMGID7778fgwYNEu8dPnwYX/jCFzB//vzClZTpN2QTGixuMXCarXHy8nDJO6RY3LBoVDXeHtK3mBQLvejh4qVKsEANAZar3CXXxKzGcBdsBVRo1M1hSkgxC4s7O8yCcHpdkkd0TrVc5rGtGDzGUkKKmRe8Hh8DvIR5TGAYxl3MCvV8BfFqP1UF/NnkaFPzRkTj3rTyNl9PMkl6bmsW1To7q3KYEnWnhBRT6k291+q98SKyrKU2IcV04bsTQzU9RB2F1ir8PKwaCjhtD0kPF/fuizyWGrol2+tWFR5uKAhV1DYoDyNzuMhzq2OBHoLLidDQ5OGiKlxU5Y18bRb4UT0FU/Kq2Hu4RB16uOi5iYJ+PT+RUUkdxwjswok7bsB5La/AXxJD26rZQPnvgBFnZ7zuIwE1zJxKUpnqlsIllrbd2ocUM3pikUJPLSuVP5ewkwyTLbmE4TaPr07ba7pwpubjmXMbyddOUJWX+ph8eBXw7jeSXzrlz0DNLEfHY/qGrKVEd999N+644w5d2ULQ89tuu018xjCZkAOgIw8Xm0TX+SgNXM3hklhgyLL1VuiubFEXxvkkfcxEus2vFzEoonIsq7GN5ubhYvWacRe1eqViw0rxlsmaMMVCOl8vuRSvFvsxxIvKC7WMVHXprofJTjFeyLGaYY5EUsfvPHO4GMJRWq9RMynK1bwRqkDTS5gTy+q5GRx6NJjJ1WPA4GljpeA35XhJPve2wqUnEY7OLqRYNvWl5nDRhbKFDCmmC1qtk2pbkbTOd68c8hYbk3Zne4ykNbKKG/3SSrkrQ4oRdGhZXqpHs8dJOmS3oGLSb81x/63yAKjXaPDUN4e3o3XIu98EHgjh5GWj8Y3gdQjGWoweLjbVoh6LrqcEXQhGDgEd+4H23ZjT8hfcHPoshrS8BD+071a1rwZePgfYwDKldGHu5P1za2izMz7QPVcyKlxkSDFT2+J1LNNLpEZZcSBnzOE3Vr+zDSkm5yWfcX506uGi/kYoPXuatbwtlL+FmPo1YNIVjo7F9B1Zh31saWlBY2Njyvv0Xmtrq1vlYvoxcpBysm5Vxy+jINIbIcV0D5fEwlM9nJc2zMaFceHK5TSJmFcwCkhieQtwsrGWNd8HEYc3ufdiXMZKwSU3tOq9V5UvVmTKs5J9uWDbDlIWjx4aUyRqGeNWIcW8Pgh4CHPd0XhSEmTLQIZxC/P4reZdyQXVWlsVjGaTJ0ZNihzVQ/agODxcchyfkkmV43l4JWdSuBRPDpd0eTOyzSEi742aw6WQ6349LJRTb6d4HANal+M431qM6i4D9m6y/64vAAw5DSgbmlXopVzDLSXDkrkfvsnKmEeGFDN7uIgQWwnFiBNvV3ntUohnULAo+WDka/N31PYh+1hIfr7mJmDLn7TjAzjJ/wqGtH4X95X8QSl76rVR35T9sbR9Ayas+h7+VPIa0AngSe078+i/xGm6fAPRHivDEF+99saKa4DDK4FgRfKgQ08HJl+FIwl1fnDb60pdu8dcCynm08d3t8rHME5IyasSy20P7qbRrGz/1E1UbxWnuV/VJYGYYt/5PNC2VXtj8InAib9zdBymyBQuH/3oR0X4MPJmOeWUU8R7y5Ytw7XXXouPfexjhSgj08/Qc7E4GNTUwdMt7xE3Q4rJRaZcEBvzGcAzqBNIIVMDOI2f+X/LdqO+pQvfO3daXgnH88UYcz23Y5gTrjk+t0UuEKZwmO+NqtjIxhI2NaRYvGBh+NIlBPYKKXlmTG94SfHsddwOV8cwTHqhpxoOKafjxfLP4WJIct4LHgm5YFAgReNKnpB8c7hkWQ5T2MV086dVcm6voua6yDcEW0QRmMvldSHbky6gd5rPZ+0tmLHm55gRAtAO4PUM3w9WAsf+DDj6GsBnfw55jYaQYlletxrSy1pBCFeUs1YeLrTeUz1sZHg2J2OUOeyUOe6/MYeL9pmdUDzplQJg1U+Adb9IOd+k6DJ8vPsG/B63IA6/ZT1TuUPowqWBe1Hx8r/hi4dty1836ut4Kv5NvLu7Az8d/xAm1v1W+2Dnv4xf3PoXzbJ72tdwpGDrdeVivq9046Nst3YhxeTb8hjmXBW8B2B6ixQliANZoVlZ7FS+mLI/j2Uem9UuZM4TZofalyYf+BOwP6GtLhkEzHsUCJQ5Og5TZAqXe+65B9dccw0++9nPIhzWJs9gMIgvfelLuOuuuwpRRqafIcdDJwOh+g23EtIbQorF3Q0ppm5qvBQOxpzYuq/jZy7Z1CAmoQNt3Rhe3XeThar0yLVejB4usZyFq6oggXGfdIIZY+6MLEOK5Xnf0lnJFEM+FHO9mjduvNlyjrnuWAnLMO5iFnrmO36rHjIyLFSqR0jMsQVzuADJxN1ALQ9dW6Yk75nIVVhoDC2cWq/q8dScGVbCbq+geYZq5ZN5LsxIwyQn+4qYQcBT+JxAycTnDhQue/8DrPl5lidoB1b+GOg5BBz3S/uvWcTKz7p9KeHRdKI9GN75Dmb6ajGsfTBQN1JzyxB160/8pdd0/T7jZ4ESYOCxgD9oKdQuTZPDRSpJHHm4mBUuijBPHMuQrDkZqoYeZu8G2RbParkJ2P9A8iQn/Abb4rMx9oNLUerrwuzYYnwn2I41sdOF0gVb3jMWqrsDt4R+g+G+ffpm/kB8FOp8k3Hs2OHCe2lNXTde6jgXZ025ErHthxBHF7aP+hEmDugGtvzR+mLf/w5QPgoY+2EcyTlc3PC6Mp8jrYeL45BiWruiJkhFK6SRJ8OomIdKJ/NlShhsh2tCp6HI9JCAwvMrNZeW05BiR/k+wNT9ivJ77j+BqonODsIUl8IlGo3ivffew+233y6UK9u2bRPvT5kyBZWVlYUqI9PPyDmHi0uJ380J+1wJKZY4pno4L8nK3FAs5JbDxaosMf3+9bVA0S4ESD6Wn04xL0LzjSXP5K7YUOs+kyWseTGW731LDRumfpb+3F7AXH6zYIvDCSB3rzcP3m+G6VchxfIUxBvnDu25WYCZ6RTyGPkIiguNuv6m68vkkZGJbEJkqZjDf5plgHZ5HzMZUvQlVE5ZbLsQbVJg7mROSCY9T7anQjYnuWbKqHxr3QosTcaafzP6IXSWTcb5x4xM85stwI77gHgMWP8rYMipwLiPWn41biHYytbwTRWOCaI9wJKLMa/+Fcwjj5y6xCMbqqYCp/8b4WhVykfUf2SCezUEF9225D13kMMlXUgxobwxerwkP6PjmxSV0Tjm+Z/CjLaEsoUUScf/BpjxPfTUtuCeyC/w7dC1CCCKOf63xEPwrrFMpQCGy1P5Q+iYeg1+snYhfIFy3LPgRPH2Y0+vw572Dpzn9ye9iOI+4KT/AWb8AIh0GL1btvwBiIWB1z+iKV2i3dprfwgIlGoh6GI92vuk5fGXau8PWwCc+F+OQtN5DTlGmj1Mcg3LaEU6Lyq5prcLRmGlcNG+79O8tngPwPRZSDEnymrT6xw9XOx+l8zBZFRaOs3hQj8Z49uKrwV/Ah8SEXpm/gQYc4mj3zNFqHAJBAK44IILsGHDBkyaNAmzZ89Gb0FKnmeffRYrV65ESUkJmpqaUr6ze/dufOMb38Crr76KqqoqXHXVVbjjjjuEBw7jHeTg4yiZVdx6UMxHxtkVjrrmhSIXwtKlvrc8SbJFnQgKuZF3ovG3i3neFxitJV1QuMTy8HDxmIClv5FOsRHJI6RYvvfN3EfSefJ5ceOSkouIPVzcCynmYSEhwxQjboeEtApHmarcz+ThgtTcEx4bN8311JWnwkVabWZ7nYY1WzSOuEkKqA6ZBg8X5bnXUNccdvWZjadKUoFHQuzCK/Dk+dLmcCEvldc/CoSbxcvWYR/BP/bdgJHBcpw/c1b6E9TMAj74gfb8rU8DZzxoqXTRFU2K4jLb/m3wJqC6Xv5VoP4V5AXF23/xDEwo+R/sxAmGj8ijSQimoQmm1QTpsi1kyiuollsK5VXBHnm7UFtIEfQdWI4z/U+iPV6G0K6VQElMKC9m7X0b4wMPJw9+2n16Umaql9XxeXgodAcuC9+KCmTO3bs5fgKOuvjfCJdMQWTtStFJae9NY4DadgzKZirjgKnGA1Gugq5aYM/j2uvOWjhm1/9p9+Gcl4FQquLLy0hDK7N8VnoxudG10ylyZYg5Ow82va8pIcXk+6rXFsMUGjkO6t5VDppeSkgxhx3K/DU7eaI+p4hxGM4VLqTsX3olbuh4DhWhluT7I84FZt3sqIyMd8haE3Hsscdi+/btQuHSm/T09ODyyy/H3Llz8fe//93S++aSSy7ByJEj8fbbb6O2thZXXnklQqEQfvGL1PijjAdCimWdw8WdUGDqxitfC+JUDxdvKlyMHi69KNS2UrhEvBNX2w0PF1WAo7lPxx3lpTGfz8mmismddMqLbLyUUkKKueQlZ1UWJ/2przEvTnuiUc+X2atwXieG6eWwfXkqNc2J2bXwUNn146jq4eKiEK2Q43xnTzS/kGI5hroyGrjEETAEHjZ+brg3XqtQizUxVYldnoRsPJ/kPKIqHgo5D8uwerYhxejcy74CNK/VXlcfjYaZfwT27XNWrunfAw6+qwnNyXvhzcuBBU8DYxbaKkukYCvr9qXmcKHcJTvu1973leH5yKcwelAljh9Xo10TtT3yvIHy3Pz+gaXAofeAeASf6P4R1uM+NGKsfr6SoB+BgA9kB0h9TN5ekeg+0becjFFqHihCbUfmkGLiPu15EnjjY/isL65JglYljyWkO/Lr076pK1v0eqGv+y7EMizA2PD7GJxIcn/F3ImGkHiH2rvxtw+i2F9yKn43cAZKEmOGHIdLgppAXo4jGZWK/qCWs2Db34FNvwPCLZoHC71PXi7UNuIRwF+ivU/Ho/c764BoB3BwOfDGx4Ez/6OFeisSZJsw9y83Q4qlMxaU46idgFgqz82eMF41HmD6L7KpkbKa5lUnbc88vDrdd0n5JI3TNOfaDdMGgxpTqMe0rLgW2P0QKpS3Oipno+KMBwB/MvcX008VLrfddpvI4XLrrbfixBNPTAklVl1djUJw882aNu++++6z/Hzx4sVYv349XnrpJYwYMQLHHXecKOOPf/xj/PznPxdeMYw3kAOgk32uuo5Q98l55XBRBP75LlTMobHUNYuX5IyqcLmQuWVS82Skr/++DqOlCjhzVeKZ2zG5Zpc6mAxTBO0cPqhgUJs3316DYMYQpiTWq2GfzP1R7UOpSfm810bMAiAOi+WmhwvXHcO4iblP5ZvbQxXq03BtZdGbadzWN+QUCsnvTW9Gs8Kim+IQCWFDbh4uQZdyuPhMSdQNyb9V7yM3PVxUoTo9SOCbJpm785Bcfl14aUYPL5VFKGZVwNMbHi62yrdN/wXsSoSnCg4A5j8OXw/JCvY5Kxddw9z7tXBRO/8JxKPAW58EznsDGHx8Sj+r7NmG0St+hp8E9yPQHQReHAD46B4FgJLBmgJn+DzLU8ljDDnwBLD5p/r7a8f/Nx7fMgenVQ/B8XMmO68cEviTkH/ff1CBFtwc+gxein0GT0S/BlIXipBivuS9VXPIhBKDgZN7riuKEseS992PCGq23ITPH3gQz/k/jsWx/4cBHSuBDz5rypJqJBIPYOuw72LGiXca3tfbE4UVjAWxMX6yfphPjT8eJaVJsVLzgXZsiq/HoGAgpX1QmymBX2/75IHjSKlI55/6Ze3hlKY1wIsLgHATULcYeLgSGPcxYPYtQPV0eB2pkDfrM/V248I6Md0x5BxpN9Sby2HOI+RoLqPvdB/QnoeqtTBw1HfE+FqS6t7DMGnmPqlwcdL2zB4uTudK3asw4EckGrX8nSZ7SCrDHStcdj8CbP69/rI1XoOlsYsw4rjfYk7ZMEflY4pc4bJwoWZR8uEPf9iwMJTuoeRp0hcsXboUs2bNEsoWyYUXXihCjK1btw7HH59clKl0d3eLh6SlRXPbCofD4sFoyLpwo056whHExEZF81yy22AQkUjiu2LDFNWfd/fQ/fGLpOskwB9TU+74/B3dyWN2hyN5XRNtOulYPeGoOA4N3Mlje6cNGequgOXS7608r0X9tnd169/p7OrbOurqCRvaVy5lobaoXnNnVw/8yqbD8e+6exAOU5DoIxc3xxkVshBU65ro7qH61nYQ3T3qmJC+HVD/UY/VJY6Te3l7lL6plYX6hLZBpfk09TNvjClq3caVjXuXaQygMcFrZfYqXUo7JHhMKK5xhvE+nT09pjE1v/FJXUPofdY035CwP905wol1bjwWFQ96Tu95qX2qa0iirVOrRz/i2ZUzFoHv8AeY1PQa5vtaMe7QQES2DNOE4fQQuRjK9Ue8fDRQMV78VHgPKXtMoUQRYZhiwvqbhLhijuwhS/co0L4TXwjchqP8K7VTP10BnzyPL4g4nUtYyJdo3491w0dW8mJz0qQJAOl9JBQrCa8Fn0lQHS8dgeicOxCf8LmcxhlqM3QNAZ/f9rsxvV1kXqfq80g8hlhUe071Vqj2JPsArabM5/A1voHAimt0h4nIKX9HvGIKYl0d2vVE0/cNAyf9FYFwG/z7nhAhyuJLLkXk3DeAinHiY6qbqngjZm/+Gsp69mIyFYhuVaPxMPG9TyF68l8Rn0BKByN0jElYhYlbvq2/F511G2pxMWLxvejJpV+efC+CzafD17YFpb5uXBK4D2Voxb8i18AXj4r2RHVBbbdHri/Fw7i/dLaf0Poj9ctgvB3fCt6AAduWiu98KvhfmBRdi1nr3wTimtyjAeOxOPwJXDhrHAZVVQnl4etbD2NR7UQsGHYSppAQPZo8dzyWaE+RaMq6uru7B6X+uGEvpLVrrV1I4SP9a+/sQcgXF9cmjkFjH7VXcb0uj32VM+Cb9wQCr10MX6xL84LZ/TDi9a8hctYLQPUx8DJy7KW5Qa2XeKJ9yDExnzqTY1D68cRmrE+UQxpThqKHEXvlYtwSfx/xEDDgpSDiZjlPsBLxyknC88hH42xXLXyJcIPikL6A6BviOY0eYj4o08ZrfTQhqXUZokdfj/ikq3K+dqb/IGVQAV/c8ToqZRxzuM+W67wAAolxK/V3mtdiItRsJKKv7whzf9Zp3YzgO1/SW/mzpTfi0RYtX8v3EOqzdSHvm6xxWh9ZK1woP4oXqaurMyhbCPmaPrODcrxI7xmzx0xFherIxRAvvvhi3sdY2eBDwyFtKHl20XO2idiIFY0+NByU1hJJL5eXXqpDTSnwl42awPSKqTGUO2zNu/f6EU4cZ0NnPRa1bsj5Wnbv8aMrCoR8wKJFe1FbR8tcjeXL6tG62RtWivtrk+VaurQeB9YXplwrDvrQ0Ji8oStW1iO4b4XhO3UdQEODdt9ef7MOuwegz1h9yIeGBq287YfoHu7K+hgbm5LHIJ57YTEqHLTFDabfvfJaHTbykOPaOKMSjiXbnBxHXnq5DoNo7Q5gTa0PDc2JUAyJvmzHinofGg4n79vrb9RhZx4hoWkM6YgkX1O5hpRpz3fu8qNN+ey1N+qwzehU2qeQ4U59ol4lJKdqIplVgqXL6nFoozfGQa+zWmmHBI8JxTXOMN5nW0tyLpDjU0se67RVpjXPs8+9IDx71XNkmlO27/SLMfPtt+rEepJ+G2htwKKeLfAKu3b70a7MRR9014uxanNPPRY1rXN0jMpYLU7ruhVV8f04meTQpEsmo+aEYbMVJGzbHrwEa0u+iCj8KfVa6o/gS9V/wvFlK1Ea7Eb52z0I+nrgQwxCRKE6G7cZj+2WzbSvux7B5V/EwffuFF4LUV8pdgXPR21wrqNxprFTu+eVQft2simxXtzYXo9FHRvTHm/DXh8a2nxY8V49dpXFxbE7clzfOmFLs1a2krYGLFqUbLPlsUYs6LoWwYTgdHPo49iwqgRYtQgHu7RrbglQuXY7Ppc//mmc4V+PwbFN8HXtR+eis/FG+R2I+CqxdXcE36n6iVC2pMMXDyOw/AtYunonGgPHGT5r3tWAGwddC39CIbEreC5WbpuJdU3vi2vMdc9YEr8R4bZHsbDyWQR9UZwbeAzoOoR3XvgMavePQHvEh1dercOBLq0ut7bXY0nLZlFHcn+Zjn3tWn3S+mvJM0sxoWENPl/2H4wP7DF875TAy7pHygH/Mfj+vlvRHAmhqzSGYQm7xVf2D8bWFh9qVq5AfLdxbDzcrZ2n2Q90m+Tzzy9+EVWKfcjeRJkizVT+neK9g41+UAQ6+m51CbBnnx89MeD1JXXanuiwz3Lf6AbDQ9fihO7foxTNer+NvzAf60quwuHAdLT6xnrSk2LzLj8aOoF3l9XhwPrk+xsOa21lc0c9Jo3Nbz1Dx1fHVhV5rwOif29N+XytItPxI4pTArfCH/wANfQGvZ20aTZACkg7pLJFOwQp/Tq0hwWB976Kd9dsQ23wtAxXyfR3Nu3xo6Ed6AwBrWFgdU89BjauSfubvfv9QkYgeWVJHUY4sOHes8ePzijQEYTYp7+zrB5Nm8whTpP96qXFi7UxLrFefHd56vcD8W7M7/wRBsa13Fh7Amfi8X0noaGzQbxe+lYddvWxDID3TUY6OqzHpbwVLmeeeSbc4rrrrsOvfvWrtN/ZsGEDZsyYgUJx/fXX4+qrrzZ4uIwbNw4XXHBBwcKjFasGjzrZ+eefL/Li5EP7+3tRu0EbPC648DgRw9aOrhX7sH+dFh/WJ6ZdbXA66+yjMbqmHE8e+kC8nn3qUZg2IrPUk6xrnjq0Uj/OlDEDsfDsKTlfy+KHVqMjHBHWdRdfPAdPHkouEk88eRzOOqrvXf/omtVynXzKBJw+ZUhBzhVbXYtdq5OJDGfPHImFx482fGd9bQve6dIWbSefOgknTxyEvsK3tg7bV+4XzweWhbBwYYbknRZUbTmAjcuSG8azzz0WQyozhzCs2NyITcuTm6HTz5iKY0Yd2WOOm+OMSkdPBM82rRbPK0qC4vWZZx2NsYO0VdW+N3egaedhfZy5+OLjbD3vmpbvQf3mpMnkqXMnazG9c+TlR9agtTtpIbHgzBmYMESTsi95fC2aOpLai9NPn4pjx3injZCFz1OHNethydCqUpS0JXdYJ500HvOnDe2D0hUf9W/vwqHtB/XXPCYU1zjDeJ+l2w9izdtJwfPxJ4zFeUcPz2vNs0NZ85x17rFCE/1ia1IJQevDhQuNgl2Vt59ah5LWbpx95lFo7YpgzevbMXFYFRZeeBS8gnmemjxhEA7tOozjZgzHwpOSOSlsCbci+Mp8+Dq19ZZTaNU/JfIMJk4Yg85jf4v/HF6NUnRgqK9WhOe4MPgwzvClFwB0x0vRg3JUlvg0QZ54RETIGlLMqMR9oUQylQFA2XBAviafAQobJkKHJf7SI9oJ/2FtHzIkllSEjIh+gMiIK7DowIdx3gUL044zWxva8FbnZgwfUIqFC2dafmfw9kPY8PZOTB5ZjYXnmZKJm9j2yjZ07G/GaadNwNiacix9biMGVZRg4cJjUQje2HoA697ZjRnqfircjOArZ8LX2SRexoafi0kL/oVJ5F0EoLa5C2/8Zz0qS4JYuHB2difsnof4ywvga9+G6vhuXFzxJ8RHXYxju5/HlMgm8ZVI6Whc2/JnhEtG4refmJm452H4V/0Yge1/Fe1qbvwPiA+/NOHdFBF/53e8iZq4JpCPDTsLoxc8hdH+EgzYcgBblu3G1LE1WHhWFiHFFL75fzNQFzkeXw1pRp7nVr4KdL+KE0dNwP/03Ib5Cy7FnsOdYj8xY2wNLjx5LF59Yq0IzbRwoXWUDnVf9e7LW3Fq9TacF74WvgFJ7WIsWI2D/kkY1pNM1BKvmoqB57yM0YvqUNrejfkLpmPyME2St/u17WjZ04RTT0ndw9a1dOG1p9ejNBjQwwpKzjl3plj/SVbuacJ73dsxaUglFl6she56sW012mn9ffYxGDWwDM82rxTryAvPn4nQpkY0bGjArJkjsPD4MXAfitByI8I9hxAkb5emFShBK47v+R+tnsZ9CtFT79M84DzE6uc3IXagHWecPhnHKXuN6q0HsemdXZg0sgro2ZrXemZLfRve7txs+Zn0HjzKZuzpWbkftWs1w+aPBe7B0UFtPAyjFC3xGlSXBVPzO/UchC+hQIkHKsVYS21SeDiGW+GLtCFOXi10L6Kd8EU7gWiXlqdHEo/AR54xiOPk8O8QPWMx4kNY6XIks+mlreiua8HI6jIxVh09dSgWnqZ5yNohxyDZzufNPwrThmeWJ774sDaWjaguQ31LF046eTwWmPa6FDHjP03auEtyBZIX7XhfU57PnTsRp0wcbPh+4N2vwL9TW5/GB8zAyPOewLRXaxGr1xQwZ515FKY6KFsh4H2TNTIylusKF1Wjs3v3bhESSmX2bOcLpx/+8If4/Oc/n/Y7kyc7W9iMHDkSy5cvN7xXX1+vf2ZHaWmpeJihxsQNCoWpF18A/kS840AwiFDIfnHj8ye/K14nbNL8gSCCwaD+Wcznd1QuWiCSIFUehzZM+VwPWcpTGciikcpkKKsv4Ik2RO6Sarn8gcKVK+V++VPrlwIP6N/Js/7zhTbQehuCL7eyKMfQXjqr3zhy+92RgNvjrz+q9VOiNBRAVzhmqG/zvRDji03AYrK4dfO+kau94XiG/ql9RvFoaUFYyL6bC1GyOZZjuV9LgEoWPdyuc4PGILXuaK7kuiscvM478jCsPxKv8xu//Sl9ll4azkF5qYNBWyV+LK71+9KSELpjcgzIcT1SIEgtoV4T5b+m12UlDvpQuBVYejnQkjDPLhuONTXfwXt7uzF9RAVOnzxIF3gLgZoQrnUCPYeAbX8T4ZUC2/6MYPUszA0cwpWBO1Dm6zScIoogDsZHoKpiACrKK0WomfqWbqzsmI5no59HOwbi15fNwSCzMQydl84pwpmVpA1x7LNz89x4N7D2NqFkUAnu/idOCaxHyH8BQiF7V0Xav4j7Hwra1mVJSNtf+Ci3R4b6lmuK0lBItClx33wFbE+y/CWJ8ou8JZ9J3u+qqfDPfwj+koTrLq3DSrS1A62nsi5XaDRw9nPAi3OB7oPwH3gTOPAmpOlc1F+BppMfQ8srAZTT+qlUMVc+9R6gax+wfxF83Y3w7fiH4dBS/NVdMQ2lZz4Of4mmhCgJafVIOYNyqUcttAywjPyuIgF8LnAnKnyaUmQ4duE7oR+hNTYfPn+ZOA/d7/KykkQdpR8/5DprtG83ruz5FnzxpLKlI16F+Lzn8MKu0Wjc/BSO8q/AqeNKMfTknyJUORKhYIM4RyCYXGvIMbK8pCTlWstKtP0k5ZWR4wEVi7qBz29qv4n9oN4uxPo7iM5wTIybdE1i70zjSGkJQnJPX+h9YWgEcN6rwFufAmpfSBZ3z0Pwlw0BTvofb3m66P3LONZSvcr6yns9Y9q7i9P6fMJgU96jkNJGdHoOY3zHYpwSaMRQ1OJDwUSeZV8A/wj+D5a1z8ENFxyNKcNMQmIKq9R9EAhWwRfU+qe5xjPeAWp0S68SeZ0oVFzwrY8C578NVHvHUIHpXWh+lHOp07FEX3+FAtpSwOmeVc6zQa3vWMm7woljE2KtpMgtaX42fH/bvcDO+7XngQr4FjyGUPkgfYwWx6Bxso/XhbxvMuK0LrJWuDQ2NuILX/gCnnvuOcvPs8nhMmzYMPFwg7lz5+L2229HQ0MDhg/XrNVIE0deKscc4+34nEcahqTQmZJT2XysCfbiWSc+VRO2m8uSb5JjcxnyPbZbpCRnL2DyTPM1W1WBeg/MiVh7GzWpaq5Jas31qSbRze533mgv/RHZTYXhqkzMqdzvlETvsTgSeT4z9ien9ztTwj65aVUTLCeT8vkQjnovkbLa30NBP6I9USXBplTAeKvMXsZcV+r8wjBM/qSO9TFXjycU44k5RgqsZN+mcdwKOa6ThWVWiYZ7EfParosmJCWZuy0kVHv1YuDQu9rr0EDgvDexZ1cV3ty9F/HKoTh92iT73w+bByy9UjwtW/FtfNVm1/p46GY8334+vjR7Ek6folmZ/ueN7Vi67WD6uZoS3tMjV+h+HX0NMP0HiXwvFIfqQeDdrwul0cjo+4gt/xIw/2FbIa5c+5FhhR2y7ThJoC6/Q7+RbVFdV7hNTyRRfrJip3by7jeAupe0D0uHAGct0v4qOEqQno7qacCCp4FXztOUcwmi8QB2Tf8LBgwij5DVqfswElyddj+weC7QlhoeiTgUH46W4x7BxJKk97000M+1X4aVcWZZ7CJsip2AC0oex4XBR4BIK4b5alG+9qvYNlYTWFP1lBx+F+f6H0eUwtRtWYVgqNToXSVyEWkeV1WNrbg69CNUxDWPomb/GDzV8zl8EDsbvxh6CoJ792Nt/HSsjZ6OiUcdhaGVAxPnkfdBKWvihZrk3nzf1GqgKBXd4dQE1SLHkqldl1AcQD3/QfI4ZOCUbBMoPCUDgTOfAbbfB+x9Etj/rPb+lj8CZSOAWT+DV5B9RE24Tci5wo2+bTUPlgb9+jgvzmce6yl+3Qun4tTWLTjVPIQedyd2bjyVZgrr8lG7zTf5N13/qX8DyGuy/mVtrllyMXDBUs07kTnikE1Njl1y/WWHmtSexrHOHsqx4qw/ye/J8c1q3FKbPvUftQ/JuVlweBXw3reSr0/9KzDwmJR+n3G9xXiWrFeZ3//+99HU1IRly5bhrLPOwhNPPCE8SW677TbcfffdhSklubju3o1Dhw6Jv6TUWblSC2MydepUVFVViRBgpFi54oorcOedd4q8LT/96U/xrW99y9KDhfGIwiXDwGY38NH76m/lwi4TtChUcbJ5sS1bjAbq5OueFGUOPIF5Q1PIfbz5mq3un1pPTu9bIb1/JLkuWs1tyCyAscPc9lkwXThkXdPCJZDYOasLMfNmgzaDZTaed+b7lO99k6emRRv1DbVdyP5TEvCjE9GCCk1yQb12KmMXKJFq3KCA8Zrg0MuYBYJOxxKGYZyRqjDPr4+ZDW3odSguLRj96CJXkMQcFMwkUCPPmIRs0mtjvRyLpGGAFMTZKgmiPcC+p4DVNwEtiZwXJMAm4Xv1NPh9WgiajMKNSVcAB98DNv/e8Pbq2Oloig8THhIb4yehtuojQHuHPp9aGVg5NczKCT+tFxJrhslXAlUTEV9yiQiN49/7KLD+V8DM69KO++nCK0uhi5N2oRtqkIBHV+ChYMj1k1AKrb8D2Pb3RKFLgQVPacoRu+vJZ30w7HTgkvVAIqzpQ+/uwZqWEbh86AWoSafQKRsKLFydULj4AF8w4eEUwC+e24Sd7TX4ceUUV4Xb5nGmCcPxQvA7uPCSn6P9qTmoxGFUHVyMicHfogSX4qymG1D26sP4rBw03kt/fBELRMriaubgwdC9WL5P66Mk5FM9ttXwTlaKL9kerby8ZT2o0NpPKFxM1yj7W0gREspzk5JOXe9QW9WVP721ZiRF69Qva48d/wKWXqG9v+YmoGIMMOVL8AKyf5kVHroy1YXqspoHS9IpXMhD5e0rgNbUPCy7B3wY42f8AP5Nawt/PwMlwPzHgJfmA01rgLbtwJJLNQ+moIcSXjK9ghyfk0qQeFZ7WCe/SZ4L+l5XvLZo5/JYNLSJ6Do+i7E03AK8ebkWMo+Y+nVg4mf17xkULjZGO0w/VLi88soreOqpp3DSSSfB7/djwoQJIp4beZJQAvpLLhFpCl3nZz/7Ge6///5k3OXjtXimr776qlD8BAIBPPPMM/jGN74hvF0qKytx1VVX4ZZbbilIeZjcUQezTItXu49pYFM3Vs49XIweWPlsas0bd7PCJZNmvbcotGC/rrkL72w/iAtmjkipT6uFlroJzkfh5Qaqh02u9ZKrVbrblrZM5nukWZiktj3zZiOdoDtFwZZnG5Z9RFe4qONjigWNN8YUK88huRnTrSP9PnR5UHDoZaw8rRiGcQ/zPKsaXbilJPX74kkLYalwSdOXkwJyCh3jPQ8XWsvKMoqQnD1REZbTzgoekQ7g5bOBg0qY5/JRwNmLgZpjs/dwOP4uINqOSN0b2N8KvI3L8GLk44agM1MTQo90nqu96kU8fAGip96P4FtUTgCrrtcEgmS5GqzI6AlgJhvrf3nd9BspIy3kPCzPN7nzBWDXT5IfzL0fGHaG5W/U+0/tK124rLRUTdQelPN1xTrUosNw3bbti8IY1aTmbDwYb0UUPSmWxFK4nWu/tBpnaHxA5Tg8Vn4nPtfxNfh9MUyrvwt/KrkLaM/pNDjsH4dBZz+P6DIKK3ZYF+yp16M2s6SHS+p6WAogVcypOOjn+vrU7OEij6MoEuVzGjfV9Y1QDureM30w9k36HNDVAKz4ofaavLQi7QDlEaEQeZKSwcDYD6f04UIiqymlTbo4V1j1E9E+Lc4nWP0zYP8z4mlPYBCe6L5SKL/bUY3Boz+P8WTcZtG2CuatRIp88lrr2Kt5U772IWD4WQmFqk/5S6ivnTz3A8PnA4PmFPY6mLxJRoWQSpAM31ejNGS5z056Jtsr4+V7su8YPFyk9co7X0oqLgedAJz4W8Mx1G6fkguJ6b8Kl/b2dj1k16BBg0SIsaOOOgqzZs3CBx9oibIKwX333Sce6SDlz6JFiwpWBsYd1DEpc0Qx6y/Q2tXg4ZJjSLF8BFrmjXtquDJ4ArPVkduhzp5ZvV+EbaguD6Ys/KzqwODh0iu+4842Qblu/FIVLg4n614M9XakI9u8ULhYbAJkWC8n7dIsxDH3r2wQ8ZET5aCNaHt3UomhljEU9J4QLsVzyNRv5Mbaa2X2MmrYES+MjwzT3zD3qXzDmlqFFJVySlVgmW5doHq4yA25l5YD6jVKJVKnnYcLjffLvmxUtlTPAM56FqhK5uSUP3M0P5AV86l/Q0NTJ25+ci0qS4PwRSMGb21p+akKPXpMIa57ezyNj/4QNoY+jRnhB7U3dv2fJsCb+09DeDErTwAzScG4xTVQRVA4MxFiikJ5Jjxmok0ItdZjom8j/LEgcJg+TzzI26jcPr9pNlDbHoUdOL3xmuSbc34BTPiU7W9UwZPI/+Fz17BGKkhE1WSxrpdrRXP4pnwVV2aDPLXv7A6djieiX8PHg38yfiFYhce6v4Cm+CB86oThqCohAR0dJ6bdb3qeeL37QCve2dmKg8MvxzfKRyLg22Yfyka5tuR4Y+XhYh9STD2W/J7dOtrgXZP4rlC4JD6XBjt5h5nLl6OvBtp3Apv/W1OyvP896+9VTQFOuw8YPq9XiiX3GIYQROq9cyOkmMXYaPa402/jjn8D627Xnvv8WD3hT1i8MTm2f8iv5clKhjNE4akYqyldXpyneQzUv6o9XMMHTPkyMHohMOp89p7xKLoBo8W4ZoXaNvU9q4P+pO7d7RTO6nu6wkUZe4XuZMOdwJ5Hk+FW5z8i8s+pqP2eQ4odQQqX6dOnY9OmTZg4cSLmzJmDP//5z+L5Pffcg1GjRhWmlEy/QrVeiebj4aIcx2ox6ySkWNRNDxfTgsUrgkZzOd1eyx7u6BF/mzrCFuHLrDxcop7x6jALz3PZ+OUcUizH3zH5xUC22gSYLV/TCeFkm5XGKXmFJVR+Ki2F1XFDDilyQeeRISWtgEPiVa8cLyM31lR3mkCC645h3CTVmzG/NYh53Uf9VgoV6a+eeNihh4ubQjS3UOc4afVsG1Js42+BXQ9oz4NVwBkPAqMuTMmTkk2ILMtQWX6fYXy0mm+s8uv0NptKPo2pJyxE8P2ExfzOf2t5aaZ9PWX/YhtS7PBqjF9/E24MbkRNRzPwcBsQDyeE7gnBuxQEn/JnBKNl+G7wx5jx1lvi7RtlTldz6tWxHwEmU9gk0kpEtGPF6G8EIntwtFt7noFpjftwUegehOIJt4wJnwWOsQ6fJlEFT9S+SlwQJEUtDGsy5U9Kt6ZxMwSa1ZpStlk616LY53H20AYMPvBYMnH9gifwysuDRV+7dPwsVFUbBXEqe7YewAvbd+DYwEBD+ekvjUGqJ5plSDGLELtW3mtmRZTIE5R4zy6/ocHDJXHNNG7quYb8flFGK2+bXueEuzVPtIYl9t9p2wa8er6WoH2wFm2lkOjt2iaHixuyBtk+KQymlJOYPZxEOObGpcAyJdTa8b9BU4w8SXbrb8m9gB6Gr7c2LuSxNv8JYMlCINbt8sHjwLa/ao+ykcB5S4Dq6S6fg8kXuZ4IOg0pprTN7BQuqeO41c90D5dEV1IV/9WHX9I8XyVz/9dglGKpIOeQYkeOwuV73/seamtrxfObbroJF110Ef7973+jpKQkowcKwxBWIXPssHMtpmOoGzUZ3iAT5g1XXgqXaJGEFEtRuLhbrrYubUPW1h0xLMKpfqzq1+jhEveURXkuGz+zwMDppt7t5OtMtjHNUzeY+ut0Hi6m0Cr5KA2t48fGUpP5eVR5oVuDKqHaUheh3iqzl5Ftq7wkgHCnMeQGwzD5E3FZqWkVtqpEESTSnEPv2fVlNVyXFgrJA0LHtB4uAVPoDmW9tO9ZYOW1RgHCGOsw07kkyJZ1KHJS+P2IKB4sVlamqcqwvqnT+PhPA6Ey4M1Pam+89x1NOUKW0kq5LEOKtWwR4dmqew6hmj6mr0bSCIJfOQ830nMnkUf2PqU98mSOIX/IbODUv2S0XFIjo7i1RlDDt6gKk2y6kqq0Uck3obtVvkop4NME1D5sn/rf6O5qxrDWJfhg2C04ZeR5CAZWAOHM+wOzoij1r9+y7q0Un5Rfxa49WoW1ku+levsZrc3VY9K4qYe+S4whGcPA9Qb+EHDWM8CuBzWFI3lr0ENYWMWAbX8DGt/U8i28+QnggmVaTqACIu9Nag4X5fM8Iw3JfQflrtQVLiYF8IDoPuCNjyeVGVO/Ckz/LgKbGg3fk3ssK2VewRl5DvChTUAz5Q0T7m2JQdPJcxhfy7/N64H1v0gqtrvqgOeO17wEiSEna16LoQG9d52MJXLo0A0YMylcVKONLNqrMRRZmpBiibfMIcVGYBdGr/tyst3N+rkWqtACdRhmD5cjSOHyuc99Tn9+4oknYteuXdi4cSPGjx+PoUMLO+kw/QN1TMo0GNqNe7RAV38rwxtkwrzpzWchYBa0mhUuXhE0pob5crdcrQlFS2tXBBUlAd26gDbDliHFlI1DX1twm8+fS93kmtPD7H7vJQFLfyMZskUNW2AMJ+c8pFhiYxLUFC753De1vZmtZNTjJkM2eKuN6Jv8dCHFWI/oGNnuykJ+tHT2vQcgw/Q3ZJ+SSs18E6mnhCgTSpyk5ToJE2l5ajd2q+8bQl56ZP1o5+FiEDZQWVf/FFh3R1KAMPOnwLiP2h4zl+uUczbNh2bhoyyXKvSwujd9xvjLgek/ADb9VvMaIcElhVkbPj+Z98zs4dJ9ULPW7jkkXkbjAXT4BmJA9TAtIb0MD0ZJ38NNQMtGw89j/gpEx12O17Z3wo8Yzj5qEHwUjoq8WOoWA52a8aRb1FYvxKhz/+ko3I66XnBrT6Iq5Pw5Ht8cc98tDxc18bkeKjax5pNCtHC8BG+NvhfPra3F+TUjcYrijZJpr5RJ4aIK6oweLsbfi3LI9mihcDH3O5Hs3kbhIvfE6nHkc/pMzfenHrvPl7nUfqcoXhwqEz4NvLRAC5lICdoXnwac+QwwcEbBiqO260LlcJHKMVK4NJOGz6RwKUUHzmv8OtDToL1B+VFO+h+hiEoNdZYoX195a1ZO0B5uMuGTwL5ntHBzNG5GO4HOTu0zUlq//TlgwRMixBrTd8i+INtupvHa4BWZGBed7LMN+/M0oRDVcObaX6AMbfh26FoEIs3al8Z+FDhWmElYos5FnMPlCFK4bN++HZMnJ12eKioqcMIJJ7hdLqYIIUu9xz/Yh/FDKnDyxMG234tlFVLM+nP6mbrIkOENMiGPp3tg2CxiyVvjiRX7MG/qUEwaar15SFlcmjZzXpGVpeZwcfeeqx4utFhTE2ZbhhRTvJH6WqBo9mTIxaLcrDhxqkRK8XDp811G/yUZR1XZBMTThBRLcw+THi7ONsJO24AUtsjjGcONedPDRd3km2Okp7P6YayR915akfe1Qpph+huqYKmlM5x3HzPPFbQOtAptZKtwUdZImvASnhs31XHeLPQTc9PG3wDrfpF8c9zHNIvNNOQijJN1LTxcTKE1dKGHmosiYalPyrXOnqill0GvcjwlQ98B7H0SiLQCr14AnPEweqLHiY9L1Gsi6/rXLwPatoqXPVUz8YND/43yysH49aUWyZtJibLqBmDLH0Xoss2xOaha8C/UDJ+BB7asEF9ZcNKJyXwaPc1aThlS6qiKG/k8UKopdUQouPRWtS9vrMey+gE4ceJCjCq13/upqO3ILU9OXbhl8nDJ5vh2IcWscp1kg/QaIaM0Mk5T77eqzNFOnxRiy3VUpr2SGjbXUuGitC310qyE9vJcVmHYaJ0nw+mK3wtvM+sxTg9Npgju1evRQ4ol2mRybe6dsS8F6hfzHgZeOE3zdCCvspfPBi58x30hv43Q1tLrSlsy5oycB8mQTCKF1qSk/UrwZxjck1DoVk0F5j+qeQNZhDqTbcoTHktuhiujx5SvaLl9Gt/Q3u9q0Dx+9j2tjb/H/bKvS3pEI2VOUjGRUeFiUNIb33NsLJkmX6lqlEgK2jG7/4i7Q39EmS+hrBs4E5h7f1pFndrv2cPlCFK4TJ06FWPHjsWZZ56Js846S/yl9xhm7+FOLFpTi0GVJWkVLtmFFLM/hrr+zDakGAm0ItGIrWXIezsPYcnGBqFM+MZZU3IKKeaVRaN5oe7mRr47EtPvJ9XVkMoSwwLaqg5UxZTT3DuFwqzkcMfDxdk1mSd1y2SojMsu+X5LAZhUvEnrw3RCOPPGxH0Pl3jGz7yCmmDWUhDnMUvtYgoppr5mGMYd5FgvvXHzNfqQx5Ox70mor27i5QbZbk5R5w91HPVS11cFsOYNf03zEmD1j5JvHHcncPQPM1r65hJuxpDrxhczCIEDFkYJMmRsRULhkq83U974A8Dp/9a8W2qf18ISvfFRjBtKQrozk54AVCeUJ4FCFxFlI3HgxMfQ9WKLHq4u9dhB4Pg7xeM7D6xARySC2wdMNXl6KN8vGQhM+4Yrl7Vxx1Zsix/GaXY5aCxQBfdu7Un0fifyiiA3DxebfBn5hvqTe8+K0qCucFFzuMhjm/N1JJPMO7PUlv3THNZJXZ+pz+V1qVUkz2XO4aH+Rj1fJg8XVZEohfg9IuS0MVdMOktxT0GKlQuXAa99GGhapSlellwCnP8mUFLj+unMyjS3vK6sxnjyriaO9S3Fhw8+gouCh4V3yxj/jmRi7zP/A5QOSZbD1EzMCcL71R6Awsed8e/k69oXgSUXa/mv1v8KqD4GmHxlX5bwiMYc6jTTUGIwJtF/40ThknweksqdNB4uVb4m4KUPY2THHt1+IRoahMCCpzKGopP9SeS5YoVL0ZK1b9KePXtwxx13oLy8HHfeeSeOOuoooYD5f//v/+Fvf/tbYUrJFAUyrBdtbNKRjYeL3aeaJVD2Hi7yfLq7oc355fHUBO9mzMKw1Bwu8ATmycNNoa3cOEgPFzVXhnau1N90K/eqrwWKZg8XO4+ndKRYdWXp4SInULZmLxxJi2Pj5tZO0J1OMCO/Kzcm+bRh2d6EwMi0oVfLp3uLeGVQSVmsGq0mDbG6vb559hD6plf3cGElLMO4iT7WJ7xx883roQtSS4L68eVcLpO7pxM6mUNTeFFIlRzn/YZcEDN872Lsms8mY9vP/AlwzLWOwqrI+SIbYbu6vlSt78W8biF8lGtyeW/6OmegIFgBkJBlwme01/Eo5jZeiwv9/9L2JSI828+AnQmhXqBcE3BWjs8i3Eks79BaueZFygartVg+qNbEmkInOy8q+p6sInNyYnlp+YYUq0iMO+o+VB0jzB42TkOKJZMzmxQtUnFjCCOWqnxR153mxNNmjH0vcw4X9dzyOSmmVY81UXYP5q+yhfrjuS8DA6Zpr5vXaYrUhje0R8de106VyevKjfqi9uVHBNNi7+CzgbvwveAPMLb7TUz2r9OVLTFyo5n3SEr4NLNyUs/Jkyifh6Yy96E8XCf8Lvl6+VeAxrf7skRHNHLLJJXFmcb+ZPSL5NrLiSxGnQfSKXeob5KH2GejPwVI2ZIgEg/i8HH/CwywNuhWkf2IvVuOMA+XMWPGCOUKPYgtW7bg9ttvx7///W88+OCD+PKXKQkQcyQiQ0WR1wO59ZlDzEhiWXm4xG2PoS4yclW42AkD6RrU71sfK2b5G72MHlllmDeZbq5lScmiKl+SLuL2C0G1nvpaoGium1yEHNnk/7D6HcUeJyUlC6YLh7oRt7IKkwsssoQlT6107VL2+2Ty4NzbsGolqPeZxHvGUDPezIeihjrw28TUtxvDmTTeUwllHocZZBh3kUJ43Yss3xwuseTccbhdm/9Vz7/kWiiWdm6iackfD6OsYRFO8W9AMOYDdm3Rwjnpa2n6m3gu3rN6nfie5WvTMSgkTNkIIFSV+CgElI9KSXgux6VpvlU4uXMtxvo7MdB3EGf7H4U/llh7j70MmHWz43rLJl66Xg6pSDDlcDGGA0ktd2WpVK55ZAINlACn/wsoHarlBADwyeDv0bhrDXC4Ctj3n8QXfcDp/wcMOQmBli7Ha1Q9GblSL+K3BZpP1Pwk2UDfF6GdXVoj6P0uId+ndRWt8Z1et1oO86XkqwyQIcUolKH07JEKKv3Y0aQhYdYhxXTPGFiGErPzcDErhNU+Is9tRlXipct9II+l5gKRz7VcV0aFjJseG70CeXmctQhYPBfoPgDUv6I9CFI6z74VmHlD3qexDCkWi6C86R0c7duIilgAQ6MN8NWXAcEg4C/RxnEa38nzghTi4pF4Tnmc6G8srIXDivVgVPMmXBf8A6Y0rU0JTxaL+9CDcqwddTNOIgWDiZQcLmYPl/6+jj3qW5rCbes9oi7JaxEXLi9YiDnGWdoAR2kLFCOObLyL5e+EsWQaoxoqz0X+f2JG/C3xmtQv90eux6bYCfje8PMcXZPZ25E5QhQuHR0dePPNN7FkyRLxWLFiBWbMmIFvf/vbIsQYc+QivUFIwEaDnN3goA5KmfY/dguvaI4eLskY+enD3chNeTqLOKvY3eYyegHzhONmSDGZv0Xe9/aEAkYuoC1zuCgKl762OHQj3JpZUeJ0cZkUrmrhLljhUjjkbRbxpvUNrOrhYlSipLsXbnq4JK0Slc1m4j11QWdWxngFOeSp1kGSogkP4SGkgMItYTDDMHahU9wJ2yf7qO4dGVFyEygeBnbnSX43BrzxMVTvfxZfkzszbY/euww5BTj1XqBsuCaQi0fgaz2MzwR+jfNiDwOtFjvH0ZcCZzykhcxySC6ePKrnkGo1T3VszglDa0+z95FnFC5SIHvifwGlw4A1PxNvDWt9VatfyYm/A8ZdliKUT4fcfxG0blCFtAXzcInl5uGSXPO4Uw5DvHypeIk6b2Pm8H4q+Yb60/OZBPyi7VJblFbYyYgAioeLLmRzts40e0Gk5HDJEFJMtitDXkGbBM0GhY3qnW2qZ9nfVFmAVOIIhYsppJjbHk+9woCpwIKngVfO0UIESkihseonwO5HgEAFECjT+jqNq/Qghcioi4CKMc69rnS3wDDw2kcwsvY5XENpVOhzOvXruV/G6fSf6XbvGvhJ3Np4Ncj3hfjQoNE4yeK3dn1FDzXnsX2L69D1n/R7oHWzpnCjvC4Ubu78t5LGDEzvKlykEjhD21MjjWQz/mjfiePjgT/i9O3LcUKwB5V7g8AiLay+ZGwkhsmB9YlXPuyZ8zjeeFfr807tE+T32MPlCFO41NTUYNCgQcLD5brrrsP8+fPFa4ZRBemkfLBzR1bHslxzuNA6zejh4mzFLgdXaWVjt3mR15JO4JU5hwu8qXBxM6RYd9jwuqkzbAqBlPobVTGVb/z0fDHfw1yEL0nBuNZenVqly4WAVP6xcLVwGOJNmxQb1D9kl3AUUkxRlMnf51suITDSLXQTChd9k5WqjDFAG7u27UC4RUu0SxZrkTYgVA2UjwY69wOHPtBCHOjWbvSXlNSJ56XDgWN+rMV1z6b8avxb02KwRM1xQ+Vq3w107gXCbdoOUbe0o2NICzzlOX0nVAOMXpiVIK9YUQVlbuQHYhjGfvyWIcXyzeshjUYqQsmwVXpIJyXniV1flmP6h/1/BfY/iz7n4HJg0bGGt8bSw2IIjsX96Dzqx6g88WY9gbJTpCw3l5BiZFEvFFRWYY10S/3kcWW+nr428EmB5vVZN+LZnQOwoOXnGOBrTrzvB076IzDta1knbTeHqDPmSinMZVB4KHm+bMglj082SodsFTqGMDGma0m7BnOAVD6Egj6RZFkoXBJrfzUHirruU8uRSVmo9g3LkGKmMGDJ6zJeu7yXVCa7fAGqcY3oixZGTOrYKvc44voDyRwukZQyozgF9MPmAucuAfY+oa2nSeC+43+1zw6vtP8dKWLIC2b6d7UcTBaodSHqnV4v/ypQ+xwKQWdgOBZ3fwRb47MxY+xHEW/cr39m2x5M78vmkW+fKSpo/qNway+cCrRtBZpWA0uvAOY/5ijEJuMOchyUMqhMQ4mq4M5mPqLvnOt/GBf77xPKzoF0i3sSD4Uy+k92j2N/hq6h51LWM/HSaT4W2Y/sZKpMP1W4LFy4UHi4UPiwuro68SDPFsrlwhzZqPlOKLxYhVHRq6NOvhnd/ewULiYPF5k/JhPyN6UZEjpL5Ul6S/dYhhwu3lhkmK/R1ZBiiocL0ZJQuMiJwWpzqNZTX2+AU0KK5eHhQt4R5GnlVHEihTJuCO4ZZ3VtpdhQN7JSCJcuhmvSG0aGRshD4WLpzpxUBBH0fnm8GSf6XsGYpnJgcwDoPqiFDaDNHFlUdTfCFUHb2c9lJTiTZZwWfRvjOnei3DcQa+KnY57/KVyw7zWcFuzE8P0HgYfq02TkysCET2uJhtVNiwxj048UMWr/L/OqgJBhihw5X1e4FVIs8XsZtormB92TOt6BEfFdiKITwbYY0FydeoDWLlzk/18s9P1dvCRr4qciX0YXKvCpk8cl9upyHCBFddzitfxO4rn6HcvvJ16Tcr6zLmmZfeg9oG1b2utdMfB7eOPAOPF8b3wqrj/mYlRmqWwxWNZn4+FiMV+aQ4XKcVRdn1d60cNFYV3oIiwOz8R3jt6KqcOrgaGnpcR2V8PzpAvZrO5ZVMWDyA/iMQ8XPe+OC2tfqhN5eeY8Jrl4uNjmy4jnF1KMvFqkMFDWl9SFiMgN0uLalHvF6V7ZbA0tlRmGHC6mkGDa743rYbtwYuIcBm8Ze+8rc8gw9Zpp3DWfq6gF9ENP1R7669OAtbdqyhdh3GRBtANY8UNg1wPAqX8DBs1J73VFVbfmZmD7feJ13F+K58OXA74STC5txdSpUxCgNXGkA+iq0wyXfAFt7aw/aJ6Sz4NAoFTsJVbubcWGQ6Womf4FPL2mUxx/dsKIQGL2Yrd7Xw8pJhVoxXg/c6F0sJZva/FpQLgZ2Psk8M4XgcEnaVqo4WcCNUZjBsZd5Dgmx5lMbU9+Ptq3Bcc2v4+aQCOOrvUB3X7NcDHSnvo3FsaQWByfCtTqx+mOlxoMP5LlIeUysCl0HuYceyP8B7S+la4/meEcLkeowuXJJ58Uf1evXo3XXnsNixcvxo033ohgMCgUL5TLhTkyUb1M0lkNqkL4TNZa6UKKqadwHFLM5OESs9m8yPKn88AwD+Sqwsnq877CXA43N11qDheCQmMRoTTu98aQYjFvhRTLI4dLacifULjEcwpvx8LVwpG0GrRXbBgtYbPxcIm5EupMlkuOUVQ+H6L4lO9uzNvxOAKhMEDru+Qaz13qXwYeG6aZ48QjSS8YivE/6yZg8hdTYvtTGc/w/wdXdN0qXp+lyt26gKGJkB55setBoPEtbfNIG1TaTJKg0F8KnPJnYPJV6A+ogrIy9npjmIIgx3b3QooZ5wMyKKFx8RT/YlxR90uUxNsAMj56z/r3wwFcruzEwrN/if+8p4Vn/sRRJ/auVWO4FVj/S80jkpQoJJDzh9DUBXxQG8OuyoUIDj4bqxqTCn6y1s+FZPgO579RjRBUi33VGl/O69Kwh6aspALbm+MplasNNWgZ9WlgvHXECDWJO12inazGoDTQBZ++hPdEYdaYVqGjnCDvmRvhdM2ePbkodNR8SuY9Yb45XOReg8omQ3VZhdJSvZ7F96X3SIb9gW5UZPLukcNHxhwuia4RdqA8S/FwsbmPsg+qx5LXTJ8lDY6MXjke2Trnx7RvaA9pIERrVzKMIgVMVz1Q+wKw5R5N+U2K7udPTHhzlyaPMWAKYlO+r78M7rgXWCvzZPnQesI/8OjbU8U9vqRkPyYfuxCBUPbKb2JZ8zYsP3AInyobCmBPSu4dwq5JmCPPpXiY9Yf76ZSBM4B5DwNLLtb2LDvu1x4EhZWjMGODT+jrUvZb5J7aaRhuba32Ar7S/TP4u+M4gZYKhxKPNIgVRWIY3Dn0q7h1/5dx6uTB+OoCo6HE2r1N+K+XtmDiwErM8QcMYcSce7gkzskKlyNL4SKZNWsWIpEIenp60NXVhRdeeAEPPfQQK1yOYFTPBbO3h4o6+ea6eI2ZNg90HBJOZdqcmhNey/KY9wm6h0taS3frxaV6XC9gvoZ4ARUukkC6HC6KcsypcsLLIcXkhE4Cl2aEHR9DV9S4kHydyT70ldxgyjZAe4Ok8iuWOeyTCzlckolOUxP20Xku9f8DZ+Kh9AcJDgCGLwAqxiWs1UqBYBXQTRu7BqBsJFAzExgwPSFECygPP9C+E3jzci0uNFllmenYAyz7MrDm50D5WKB8hPbdaCemdLRiZiBNyAT6uX8wKgZNBSonAZXjgNDAhJUdSTX8yb/qc/rb06RtLknpQ2UwQ0qX5V/RrpesgQPl2oaGLJBoYxvt0RRHIjFoWLNOorBrHbs1BQ5tdMvHaGHXKM4yHYd+T3VH4dhCA7S6FclFtcSiyXP3aF5GlCi157B23Eir9pc+IyVVoFJLikxJU8UmNj1qO9JzuHhlEmGYfoI0bKgMdKESzQhFSrR+q1v9+k3P1YTzxhgVNB9UxBpxWeBPOGHPSpwfiqGktgSBhhAuCGyBX4RGzIJjb0Js+tXAex+Il73e/WnMm3N7ytu79zbh33u3YEKoElNN0rVcrS71+S6Wq4eL0WLfHLpN3mcS9pYoQl4vIssqc3pkEnJTPZTY1LshBJEpl0KhPAfkGipnDxcX9iTGhPemHBIOrzvpJZJat+bQW9mSTCAfEGHFtOdS0ZCMCGAOi6bmPEmH/Fj2AylwTHq4aK+1EHMWChdTSLG0ChdDeDKlfZnqRiqZShLXK54njkuKHd0DxuTh4hVjRdcgrxP/AG18rZqsvTf2I8DEK4DlXwaa12vr3H3/Sflp2Y7/w0TfrahCE/zv/TD5wQm/QWTMxwGsyhg2KVsvNamgNY9HVv3C6v0UD7P+dj8zMeoC4ITfAe9/1/g+eZK+/lHgovcd7QmY7KFxbL7/SRy35mncGmpDqCsOPE1KSIuQ1fEYpkajmB5ohN9JBAa5P/SXiP7S2hXBdv+JaBr9E2B/vaXxiGz7VkoTpwoUqzxczBGgcPnNb36DJUuWiLBira2tmDNnDhYsWICvfvWrIp8Lc+RiCCmWZmOjTr4ZPVxsJmphCWT6rCsSQ1WGBb/uVZAQlspjyQHtQFs3qkqD+rWk8zown99JSLHG1m5UlwcNCp9CY65jNxc/NOFYIRfQVudWz59NDhd5b6QlaUtXWMyfA8tzs+gxW+ZR28hlQ5pUnEgBfEzc+9rmLowaWJYx9INsi/J1XXMXhlaVeCJe58G2bjS0dos6Hl1TbvisqaNHLM4rS7VphLx7dh5sF+9NGlLp2HrDbahd7Dusue1S1Y8fXKEselLjtMo2SO8nwx0Y20FHTwS7D3UY2m5SUWbdZqh+aEMtE/YS1C52HuwQdTV2ULmuZCv3dWBwz35M8DWisnE1ut/fgiF1r+Cy4Lva7+DHy9FPoGbEMThxyjg0RWtQUxZDuHQUdkSPgS8QxMQhlSkWaapidM8h8gyx+DAwCaOOvx81W29DNNKFSMwPnz+IUDAEHy1Cm9cmKmGv9lAQ6SATt7k5MA6dER9G+naL17uGfQ237Psipg4fgMtmpyYGpXtDZZb9mcpI40V1mdafqY4ORcdg+Jbr4IuHEQtUIOYvRzxQAV+sB6UdmzRFytufQe68j0LTuf817Jn9KPzRNvjiPegpn2ppntyeUF7TR3Kj29LRhU179oF8nUj4O6qmAgPLyQLSh85wHLsOdyAeJ8GwT4w1NRUlONTeI0LjkUJwX2Mjulv3iHOXBEsweszRiASrseNAu95uh1aVYtiA0pR5akAZzVN+7GxsQk9XC/yxTvijHfDHOjCyOiTyzNQ1d6MrYj0HUF8bPbBct4Bv6uhGS3MDAuFDoskMHToSobJhqG3pTvEOtSIWGICe8il63VF/Nbd5moPp2qzWFTR+0ThW29yJAy2daFb0Z1QXOw60GeZ78/EbWrtwsM0UoDkhNKPxjsaTnQfajUmHA9oxCBobc/FilMen49Ix7MYb8s6jsY7mGxpndh3sMIRalWMhjUe7D3agvSf1vlWXhzCmply0RRrvJGMGlYt+ub+pE82JsKFOKQ34MGlYlVgTUpms7o08/r6mTj0sqYTa0MCKkJgXD3ek1r8thlBbWq4rX7wbpT21+ErgNzh1xYuYX5IYEB/JcKhglWiz8Vg3fDTmJKD3fiOXH2Ggit6IJB7S+jF0OvZ2DcHYweWoKdfi7NIUVFWmzQv7m7qw/UAHdpSehatm/wB+ZR25obbF0L5lm6R2JdcX1Kbs2ryZQRUlGDmwTMxN9HvZZsije+eBDn1MpvPQMeX6iO6bXCeZN/3ZCtntQorR/ERzwPABpRhSVar3Z2rz9JUJQ2gel+s1Yw4X4eGSON7h9h5RbzQOyvJJC/pwYo2342C7OPa4wRViTbn3cIftWjYXaNycNLRStPn9HcDGulYRBSKT4VK6ulT1XBvrWmy/K49F91CuPWXdbK5vE3Ubsxir8qErMX7bJVnPJLjf0ajdj3xQf697dyT+bj/QJsbCkdVlGFRZgoaWLhxMtA8VObZZeero3jjRuGhf6ZBjLc3Dew51irG2vqVbP7Zsj3Kel12qoaVbH/+SYWS07zS2devnpeNPGExrJ78Ypzt6omKtrl67nYeLuf/KNnKgVTs+jfEZQ4opH6keLjRGq3VD5UrxcEmMZ3SdNN6rZZJlpPV2pjp2OkfSdUwaWpVVm5f7HXUvkQ9ybSah+Xnv4U60RWZixPx3MHjXbxFfdwd8sURoRwV/5x5cHfwuAgjDJ0OTTf8+MOP7CHSE9T3MvvbkODO4sgQjqsuyKj/NCXr7DCQULikeLjYhxczh95Q1GlHfYmwXBJWPyknjdV1Ll95maZyXewLqj3QoWhvI/T+NrbLNZwP9lsZ789pGIuc+Or66dqKyTBxSkXb9Ykn1VSg/YQZCnbvEy8H7/ozylveEwVf7Kx/H7jlP2Obt8cW64Y80w5eINKD9pfVLRLQRf6xL+ytCgVI5qZ79iIt6p/vnx4jqcrFvqGvpgbbs1L7j99M+ohzBYGnCSE0+ytDQHsegAQMQCpWJCqE6p72ArDt9H+lB5NpmOpbhysAd8HfEUSEWbTQp2v9OLN8SzXdX9Sfw2MH5mDhyOI6bNBaxQCXigUpt70kGdMJQEHqb/ufSXWJt+qFgmd5eze18T6L/mcdl7bmza+McLkeowuWBBx7AmWeeqStYBg7MLsku039RlSzpBChGz5T0x7Sb2mjNZJ74SEBHE0M65EJMtdyQ79Hkct1ja3D0qAH64j2d14HZ4sgcRs1cPhqgf/LEGswcPRA/OL/3ch6Zy+lqDpfE5o4mnebE4k9drJvPZd5UORVA0UL59kUbMH3EAFxz4XSxaf7xo6vF8f74uRNyUmCp3gr0+0g0ktMGNBkaLJl34YV19XjkvT34xIljcfGsUc4UNdE4lm0/iL+8vh3nHTMCnzllPPqS1q4wbnhijX59N1xyNKYMq9L7Gn02sLwEd3xslnjvty9uxtYGbWXzkePH4MNzRvd6malOf/70OkNbnDCkEmdMHZLi4dKVWLDrVrMkmEmMCx2KoJLayZ3Pb0pZaJoVZSq0iaD6oU3FTR86Rt/UvryhAQ8s360Luj9/xkSMxE78qOfrqNp3CJfS6q8h8VDYOOxqPLDvkzirejh2NQWwaE0tvrpgMpZvOYSVe7aK78ydMgRfnp+wnlOg8v9i0QbUN6du5iR+/zRcd/Ey4fosBf9fnDcJZ0wdCux5Alh/pxbb3yJXDMWu3Vp6Ed4a+ius3rkf5/ofQjuqMXbcD4B9e0Wb+PULmyzPe/SoatGf6b7d8p91oj/f/tFZQon3pyXbsHYfta3URNJ+RMQm9Gi/TZyeDMTimtW631ID5S7lrR/gqLeS92V59Hz8JXoL4ppTugEfYrgw8BBmrnsDd4V2Y2DbQQTesJ5LSf05Q3kdE+oxHwbGfYj4yhALBDA2miq08KMEE+J0piBiwq7Lj1hZOfxlg4VHTkfEB9/BzYC/DXF0YhJt9mywHtmsqUk8zGQzSqyLnYL/i1yDTlQhigBOnDQcV86fqecd+vubO/DeTnMsgDiC6EF1eSm+umAq7lm8FPFYFHUHWnHmgQZMGDkaT32wE0vXrhX1r3LSxMH45PFD0HR4Px5/4wP445pQS6vppEC/feIgMV5uqmtJvK99Tr2+afQAIRDZnPhMGwm0z4MIi0dASOg1Qr5u1OAA/IjqRykdWoW27hgaWkkoQqVMeJCK+6d9J4YAguPHYMyw4dh5qAertu9Bua8dpehAGdpR5uvAnlAAw2sGYH9Dj/itmSaKYjKmWghAY+FWVOOQKFuL34eSqhAirZ2oSqxr5Fm1oS1ZCvX6ytEmEoFT2QIIYVxca/OaWCBZf3SMmN+HodEoyO5T7kPFdyjCoQ8YGouJz+Sq0HwMwu/LPH//nP7LcrngI+84pVxWRH1l6IiVIeSnlhlFVyyEnYM/j5eD38CafS2Aaei88NiRYs28pF4b7EeQkMMkvPr9y1tSzkNhK8YNqsCj7+/F5SeNEwLGd3cccnYdPh+uu3gG/ueVLbqC4UvzJ+G9nYexag/dfeD0qUOFwcfTK5PJkiVBi1BeuYa5kEJWqgM6t7xWEvLd+YnZGFAWwj/e2oHliWubNXagEMZZlYOmbXm8dftbxENCx5OCQxLkPrOmFk+t2CdeD68uxedOm4DfLN4Mt6H129p9TXhxtx/LX9oCv4PEyXYGE2YPF5qnM6EKuWXdUH2S4piE3W9sOQC3yTakmCyXXBO5Vg4pwE+U59/vaMevKA3i++dNw6+e25jW8MzKkl/1oLJbz6iQwo36+D1LjDmRaO8p1/zyfst1p2zr6vnkXuqVDQ3iIZk6vApnTR+Ov72x3VhOU34YXbkjvWlM90i2q7e2HhAPtZx2GHKyKH3RfAyrdi2PS+tpuaaW1y+vmQwbnNSxysdPHCuOp9ahZOGsUULp9dom57kOf3TRDPz1je1iLZ8vpFz/1cdn64qE1Xub9fGOwh3+8PyrcfeKc1Ae08Zg8T7acVXwF5jmX41Kn7KOG/cJ4IS7U+7ls3v8eDcxztAtvfkjx+LuxZsMeyEn0L2le0R5eGVOy6wVLiYFmlW7oPZ562XHiv2aGhJ+5uhqXH3BdDFW3/TUWjHHfPOsqbjz+Y2YPKwKZ08fJvbIufDdc6eJeVMqFc0cNXIA5k0dinvf3GF4/8rTJ4p15XplXsl21VuDm/Gz0BUY6DuEyqbXMWLJ0Yho4n4DtN4ahEb4fe7sTcZm8V0KbyqJ+0KoioVQkVgnRoN+BKJxTPBIbmIroqEAvh9o09eBnfHKxB7HZ1grG59r6+ldpWdj/9hfYt2BOqyrBZ4VobtpjURRHywiPyRQ1yJb6lttxy1zn0jnMWb3W/ZwOcIULu++q1ncMowZmqCdhRQzhgJLh501gep6bc4fkk0OF7OFHQknyeKwKpEAVcaztTxWNLuQYtrxtb+9ibkO3QwrQEImacGjLuzkgtx8rlSFi7NFxaubGsSxyHqArKJqykP6sag+yYo+r5wJIT/au3PN4WJOoh7DyxsoSTjw8sYGXDhzpKW3hzy/Gkv+pcTv3tjSiI8eP0b/rC8gyxa1jVPdS4ULfUb9vSHcJRbGZDVNFluSvrKCIQsTaoe0jiFrF7IY3HWwXW+nE4dWYkilZmm2Ys9hXH7S2KSXk58s4TSBzordh/HZU8aLhc62xnZxPfScBDTErDED9c2IVZ6NLQ1ton3S78iqdPpIrX2S1bBGHCXt69G+9mlcF/o1qmwWdK3xGrwf+Ahah30X2FcnwvGRUo5Ys69ZWLRJaKNJSZZJUKWyvrZFKFtoUWj2ZCBaOiNCyUKCAalsIV7Z2KApXMZ9VHsQ0W5s3LUTf3x9t/A0qamqQiAQxIfmjIJ/d5MQhD8T+5L46peUWNJq3RHU9KlMm+tbRf2RhZv0HqDroE3X2n3N+n00Q9Zt/9NxFz49+HnMH0e5XTrR1NqCjXvrhbIhUjIMUV8JokKpQOLPIA73lKItWo6O+ADsiB8jklL/5fJxOHxwF/7y6joh+D7nqGqcMCoERFpEmKGGQ414e1sTYv4SlJWWo607Kuad8pISNPRUoi1egzZU48SpE3DurKm47j870Rn24bxJcRzc/Ta+4P8ZSn2akF5ySuBFDA01oc03RFgtqgyM12FsbB1AtzXLtbXmEh8X4TEDaLfNnRNATyKEplKu7magu048pR6gW4Z5jJn+5bi95JPJN0hu+iANvkMQi/txRXcPrgpFEfCR4F97kOKCWoCoj1eBu2WzJKerV7UwAR+JRvCxEgvFEsmc92tb5q+nG4oTjl9nWK2oEzKy03IO4EsaXO3PjEzTAdXHPmASCfyszkd1cBA4Ld1x6gCRutcsb2sDxmtajqyhe0CPULrfxoBSu8/jgBKVxhXiJYOwNzAHbV1hRKJRVJWQlXQ5dhxoRXc4AorqR+0nHg2jKtglPC/q2+JoD5OVaECMTXLs3xk/GsdeeBduWNQglBrkxdzcHcaXj5qMs0oCaOokYw5pxBMX8xIJvOXagca4848ZoY+VFx07Eqv2JoVvktqmLry78zA21mrj/uL1dWL8JkbVpI6TKqRgaeuK4M+vbTN4c2yobRUeExKaX2QuMxqzdUGBz4ezZwwXXgK0FiAL4JMmDM65/unYtL7p6I7gn+9oVsCqlxrN1e/tSjR8mu/2NutrDFK8kMEC1SOt2c49egSOGVUtvt/WrXog+TB/2lAh/CY21bUa1in0+/ve2qlbtVck1v35QOWnuey5tXU4TIvKxPqYLIvTMWpgufCKsIOE0nbtwopTJ2lGJgStQV/b3CCu97XNjfpebQSVy6V+RUpAus5soDb/4vp6V3PLHDdukL7epuO/urFBHP9we1i0NVrnUB8kow7qp1bMnZwa7ofaB7WlrY1pzKUTkDcLteGH39VCoZJXDe0xaJ188qTBop6o7ZPBCXHKxMFCWS+t6ulctAYyWzXTbwZVkrdftzBkkV5c8viVJUGcOEHLAXTs6IGYOWYgzppOefkgxi8yyjGvp1S9Cgn/B1WEhCCQjL7soHpdvK5OjHULjhomFArbG9stDS2pXdCYITlqxABRrkOJvlESCAghN0F9fs64GqEUdArtUWg/8tL6erR2G8dCOdZSm5f7xUxtvr07KhSSf0soW0gxMHRA0jslW+ge0bhLSu1504aa9gGa4RcZF3XFShEoHWNokw/G/4SvdX0Ow+MJBcCwecDp/0yEutS8Ns6aMRwb9jeJCLxiH94VFcck5RLthagvjFDW3ukgD8yZY6pxUedI4Yk/Y+QAcX+3NLSKfcXssVYmM+RtVYHjx9eIdfyQylKhDCROmzzEsl3Q/aJ9FykxSNki9wfkIUXKcjJOpX2LnKdozqJ7SQJt6ckl27wTaL9AdfGvd3aJ+2HejxDUpzbXtYqyyeNTM6Hv/2fVftEW7PYjzhiHh6N344vdXxFKlcE+k1Wdx6CoAmU+ZX8S1Yy8cln/9RqKZ3Fs5Pn4h+9/sL/ZuP+ygtY2pJQ9aXAF1u5vEzINJ9D64syjhomxlvb45FFmd3xaO5mVLE6NVeTXsjVoYLxFTlvAN954A3/+85+xbds2PProoxgzZgz++c9/YtKkSZg3b577pWSKAtVKIZ3CRZVPZlpo231MvzP/1klYEqmkoUUUjXt0iGhCqCzdyek6ZMzZdEmLzZbt5jBqZoWQPD4NyqQ86K2QS2bFkJubG1pIEiMHlutCAKOHS/p75CSHC92PdxWr5be2HDBsBmgDnZPCJeo8PJTTHC5SEC69J2iRRgvHY8cMzKioIeG87EO0IX5/12FN4N1HqOFoCNUNW83dc6C1B6Nr/Ib+L93TexvZx8jz5rbLZolN09JtB8Wimfr76VOGiE3Kv5ftEhsxUozI+icl4ZyxNWLzSItzEvjT5u/NLZpV3KmTh+BL80iUqbFu1z5hOe+jy6Y8HpQDpH2XyPdxsK4Nc3zLMc63GdFlJKkcDVROxKQDe3BCcBmO8q/CAMrK16gsEBHEm9FL0IRhqIuPxzbfyTgQHYLh5aWYH9Cmatq0SQXRqr3Nos5pLKMNFfWDZdsPpWyU30xYss6bNgxXnDYhpc5WJ5L6yc07WWXS5pW8yqhNkiu5TqAUdeFBaEcLZo+qwffOm6Z/tHJPs62FLW30v3BGsu5Isf2dB1YIJTmFttlzONm2SBDZ1KmV5ZjRA3G1hTcghYX66RNrcf/hT2DWebNFuIbl6+rw0K49OG5cDb5zbrJckufX1uLV94wh0Vp8I7AtVoEtcW1D3VRfhuNPO1b3SHpsyTa8FzuEc6ePwMdOHS+s415YUwsk5AGXzhmFZ1bV4tCeICqHlqA1rG3iXt8fREf0bHT5f4/vDfuXCNGGkkHA3qdFXpnJMQeGK5QHhnLMlA4WE1WjCGnVhcoSstj2obmjG9VlAYysLhXhug61dSeUDHGU+LqF0uFwbAgGD52IWLAGm2sPYGLpfqFI8sVjGFUdRDgSRmtnF0p8EQwMtsFH+W9I6BAfgBYMRk+8DD2+ckwaORzBkiqRl2Z9QwS1rYnwZ8IifogIL2aeG0nIQZCQisYy2vQeNWYURo8cj8fe34cKNKE6oIXZmjN2oBCg2hMH9j4BdAqzs1S6Dwr9gAjplC3RrmwdHo4oSGFJ06LmEeNDIODXNo0yv4me40QLbZd87RNhGHa3V4pwGKTQLA/GEuGiEjfK5xNjdluX5s1DDK8uSxjE+NDcFUkYcmiWiDTOlYUS2xZ5Lu1F5vfoL43R9KiaDN/M6zCuYqwQ4Pz4sdWIdwDfPHUq/rhnq9gI3/0JoXbCDx9ehWhXHN+cOxV/fHWrOMydH50jlBLf+reWa4X4+/AJmDq8QwhCqcwkvDxhQo1YW9CYpNdnLI4fPbZat5ym673tsuSYQ5DnCj3M3PrMejEuy3lOGrlMHlaJn1xyTNr7SPMZeaHKcZ6UECT4W7mnSaw3aLwmIRSFoCFBF4UUvPUjx1qGsch0LidQvZwyaTBe39yo1wUJXOn6aK1B8wLVFZWTclqRdbEUgNLvSHhtXleR944d44dUiFB6JEylOZ4E0ySUlPVBXueGuS5HaE6++uGVYg0URxyjK4CbP3QMQjkms1axaxeZIEUNCcmvfWSV3nYojN/NH55pG/K2N5g/bZh4FIqzpw8XD+KFdXVCASLvN63laH3nFKqnzytrmHT85fVtYi0m1503LDxahE+SUNhGWk+qbdOuT6lWzZcdP0bcxz8u2Yr3dx5OHN+Hn15ytCFklYw6oK6d6HtWHtDq/f/ECWN1wWA6aF9i3pvceKmzMYH6stWajqC+TV4I2UCyBupvsl2rYyHt5659dJU+TpIihsa0dG1+W2MbfvHsBr2d0HqaIhXkCnmjP/b+XryxtVFXuFA0DSnUpzFNnuvKuROEV62B1sXA0quAkhpg7j+1PBIKtKYPh0dj0aKdWLjwGDy1uh7PranF0oRhFhnJpRsXrbjo2KTv8lWnT8z4fZojvn1O6n2jc1u1C/K8ofFcGoxNGlaJ6y8+WsxPNE+RN4zqpSjrRz63a/N2kCKHoovI49B9uHKu8br+8OpWfLDrsLgfJJv52YeOEeucqx9aqc9Ps8YY9zzZMwvYEQBW/VQYiVlCbbNivJZbkvYNvqAx7ybdfzUUGOUHEjIILSdJLB7Di+vq0B2JiFUTGQ+fddQQLSRqPIbXNzegqyeMWaMqMJrEJtFOdHe3Y+O+Bt3jekSVD+2d7SJ084BSv2G/T+uefMK4FwKSvaltxDdgGkae/m98syz7uYXG6lwgjzgnGMIxOpx7k+EhOaTYEaVweeyxx3DFFVfg//2//4cVK1agu1ubOJqbm/GLX/wCixYtKkQ5mSJADallDq+lono9ZFa42OdwMcvGO3tiWSXdpEGMhOVSYG5UuCTcKGNxsXFatuOQ0GSrHgfRDAoXc9nl8elt2tDSgrg3MJdTTS6ZD3R9ZPFNjFIsmFRhK1n60YKT6o4sysx1JIXH5OJLbtbnHT08ZYNPwjoSCMg8K29vOyCEfJm8KUix9cbmAzh7xjBDHg1a6JGVoOpdI4XuVgqX/9/efYC3VZ5fAD+SvHcSj+y9JyGsBAh7hU2hpaWMQqFQWvbmzyqlbEoLZXQAbaGl0JbZMMKegTCyyN47znK8t/7P+aRPvrqWbDm2Yjs+vz5usCVLV1fytfyd+74vHyfP9mdrKnvmjpN7iLoNW+z2cpGWIYzFRSP+ERia4RJcrLSBhf0+LjzvSuDCag5+L8OFypp6fLNmB44YlW/2ExcyeN98w8o3KKwm4P6J1JLNGaC497OzEmJLaWWjMwUj9fnn7b29YLP5Xi6i8OynSHjCLq932Kie5ueE7UZ4BisXwXjmyUdLuF96NKrkINv/2r4h5P5j4EI8m9Au6u47sLsJIv41a21oZkigNNiLyYN7mLMuX/p2vQnLvgyGffYsPFRuBWaeh9EbpuPJJH+gP+y/w7fjOH7YzeMa9sLAfx7J/4vwnmmbtz9e6vYsPt/Y+OwpPlf2DZfzNcuzNe3CCZ/rf3yxxpzxzH1/1JgC87i4r/n808FRXktscWhbAvK+jhnd05zlxT8++IfPaXv3xXuLNmPv/t3M4uDWYCWK+6w/58k3/IPFDl819x38Q9P5mLi4xTPK+Jp0vrZYkbQ5eIaj+/ucZwPzZ5GLm3x+2bbPBoJcvIh21uq/v14fdlxmX3QuwjnPIH/ms1XmOM+rsdLJ+dzzX/4xa7YhJwUnju9tjjHc5y9+va7Rc1PZfSo8R13UsBEr/w7M/ElgQGo0/GNr8jNAwWHh+6y0Cvf/e64J+Pj7icfSKycPR88+2fBU1ODuF+dErF68ZZ/R5uz6v65bhOSaQKsIvpyeOH4SfH7gVy/MNsHXVYcOx46dRXjhi6Wo8mXDZuNcALjk0CGh2/Nv2Il/BFvwcMHz8MmNF094NHhn/VwTapam9MarFRtMC76HDt4LvkQfFiz7LvCcB+9j7/0mAI4FqYjG3gosehAoWwXU12JrcSk2FpUgJ6EcfdMCZ/vW1LO6IBlpyUnBfssMAXyo9qRh1dZSc2ZhVUIuRvTqhtnL1yE1yY++qcUoqvBjm6cv9hpQEBqvwwVfvjbqfcnYWZeNal83TNtriOP3k8dUwP7rq3XmtcJAIC0pEaebBdHAIv83a4swZ+1Oc9mkAd0xoV+3sECCwzcZYsKT2DDXh19L6Rn4ut+PV2avxZcrtpkw7aS9emG/gd0cwz8b/n3+i+VmZk9WYgXqa6vRJ68AJ0waASRmmWG9y7Z78Mh7y0xIPDI/GRceHL54uGJLaVirjlH9CnDuYfuhsKTWLFYQz+5kOBBpBlEk3FPvBENv+unkwSZ8dSotqsCtLwfmRHGu1e0njgndfk1pFX7FMMQfaMF4z4njYr7vWDEAYnUEF3ie+jRwJvFe/XNCv1+4KMtjob2M17WLp8MKMs1ZtxYXcmxbzf0HdY/4e5XHRv7+YlBrj3GxLnrz9hlION8nBL7e/MICt9su8PHuztyvH+6evih0rOK+5+9k296Jz1O8e4bz8TBwoaEFGZjYr5t5fOyVz1DdXofVpLadyz4Du4W9n4sV9/Nz2wKPjY/zwCG5oRZDDHraImwh/u7gz/pnwRY6I7I7Rqkg3z/xOX1z/qbQe6P2DFt2Nz52vhfn70i+P4x0ElRb4b5l4GJPGnGGLS3FxWLrgMGBxfiDh+aZwIXG982OeeE5EmfFF4PMzobvhRhefbAoUDHg/JuJr/kpQxresx00NK/Z1/zg3HTz3o7vBQPf07qT3vje/L/frMOyzaXmueTvUL7vpOPH9cLzs9aY4zj/Po4YAGYOBY7+NOb7s1Vy9rjeVNVce7GV9qycMZ8H/y7jsZ6BCztD8O9o/q7k+3X+/nf+vmOHgZa85rnP+fuFzwFF+ruaX+PveeIJEvbvwr0HdAu17DxoWOS/WVtk0NmBjzjhb+zSmnVm3YVOH9UXnmBbc77yyxICAeAHxSkYn5Vt3qxvKK3Agtri0D5OKfeZ/c+TRh46ZQKefnsJlgff1/zfMaORHawY7SiS/H48/J95Jsjkj/c9h48HUmKr6trdnCdae1o6w0UtxTq1Fr9r/fWvf40nnngC55xzDp5/nr0cAg488EBzmXRdzgVaZ3ux1rQUiz7DpXFLMTu4sSm2qsDOcuBt2FYPzmGttpSVm/r8rLXmFy4Xp51na7gHvjfXUsy5CM3/3m2BiytgaasCF56xbBcvbW9tyw6E5ePkL3cuQvDMJRvEcfGNrxHuf94G+7jyv7mgzgVeJwYxdNzYXib44HOzNPjGiSINwKN/frHGPGfbyqrCzmb58ycrwtqfsa+z7YUbKQDkojsXs+kv5+3b6HL7JtB91sdZ+w/AXz9bFdar2OJsD7s4muN6HZy5X388N3O1Wcixb9BbgvuI/df5et5RXmPKtnlWCt+AMQDjAEkGHvwjgAthPHsl0hlcDGvsGWMsC+fiqW0fZkv3iQvw+ZnhJbh83vm8Ov+44X3Z3un2NcOFc7evtnqwrXg9Pl+5I/RHD/8Y4RviDxZvMa8nDkFlX1+3IlfgwpJ4nrXLbXf+4cSzKhm42MUrZ7ku7+edBRuweft27Ni+ATnYif4ZZRhevQaYNx9Y9iegYn2rKqv9idlY5t8L31ZOwDr/UHh7HorBPXsAG9eb4wLPgLQLTOlJPqQ7Wp1wlzLQtMcb/kHFPzhf/GpdKODkc8oz09j6ha9P/gHp/hm1eBzkvvnf3I2m0oD3f1Dwjw+eJccFJJbUs4KFZ8rZs/PYOsB9O84/gp3HN9uKzt2GgIELF9c4tNh5XOCbffMHaJQWBs7FzY+XbTVn8NqfsWh/YHJ7+Ph4RrfFs9vt8cPet60IsrgQZ0Mc5x9ufA1xQZIzD/gHPbeZr3e+KbbBWKNt4R9aPY8OVERxQZ2L6+7e/qxsidDvnwvOo3tnmdcFt5OLOFxENY8tNdH8kcjnjM+X/V3MN/cM5GxAan8vc8CkXUy1ixX8Xv4slyEbJ4/vg/cWbjbHWnfoxfvskZFkXmvRArHAY083P3dvzAss8LH9kD1hga9F+3zxTHr3MTCi1AJg4n0Nn1bV4tEXZqO20o8fju9vFor5uvvtiXvxByTsW/mn+StvLTJVmN+b0BfDRubipaVvYH1NAZL9Ceb5YoA38aCGs9vSK2vwF1Y3VAeO04cPz0fC6PAKMW71osXzQsepvfJygKENZ0D2LajEY2vmmdfFqfuMbz5UimDC6N54dcUC89jGjp7AH66I1xs2ZjBmvL8cCJ7od9nYYUB+w8/PkHQ/0rJKzHMyfuRgIDN88aBnci222G8GMDW/rznDsmd2QihY4Gu+pYu0/B4e+1ODFR9uPNbZ3zHu2+drnkE5X/P8eY/XAvHBw/NM4GJPluCCpjV1WJ752bCXOcMNHnecgQuDfL4O+XPW1ELdgcHjLR8PF+RixRCHZ+nz2G/fX/C4xZZEzeGxgNvEYznP1OUxme99QgtzPdJwwJAeePHrteZ3xu6oruXiJodT84Qb7mcel2z7MP5M2moWVp/y9wGPY/Ys8ZZii60XZq0zt8v9YI/rDFV39TajmTos1wQuaYkJGJTeMQIX4uNkpQcXcNzB556OC6j2dyR/Z+/q7KFYxPo7MhY8+5/vWbJSE0MhMNuN2fC0tbc/JC8d363faW6PP2OdEX/e+B6GvyPdoRF/1lndHOtrnsdkBjOcwcn3erveQiqAwQCPt6wk/2TZVvO3FrsCEI8/PJGJf6fy94CzKnxXud9zRnvf357s3w72/ag9EY4/n/Y4bz/n7zqGMPz7nyeA8cSaXTle8/cL37fzbyH+3nFjiGNPPAv/WzHXrP/wfWpTf490JNw/b8wPvL9wv+YDAeB6FBZX4p0F4a37zpjUz4TS9r2OPWmE+4CBC0/KGNgBX0/mZ3ZYLl7+dr059jZdLd+++DcQ38amJiXE/H7WtjrtrMdnCWjxs7d48WJMnTq10dezs7NRVBRbb1nZMzkDh6YqXOraYIYLb959Nq/7jPxIGgZkNwz7tLmJMxBxngVtqxO4OMvHaKtfGs1waTScPnKFi/u/483uYxswtdUMFwYuxP3BxSou8PLM+NzMZDN01ckOZLVvsNKTElBVE3jTyZYydhv5h4U7cLE9udmflouMfDO2w9Gyau32ikYt2pxn9n8RnG3BNw68Hxu2sDSfb3C5iPn63A1RW5wVOcIZ3q77l559PduzYvjccpHzAPNmxRt2Bhn7P3ORmYu69rXIhQ8O5dtaUmX+SGM1EPfXvHVF+HbhtzhufD8gIRNIyAistu/8Dij8GChZAmQMBXrsC3SbECg1rq9BUdEmcyb3Nkep+uI16+ApXWOGDhfu6GUClxXBsGHm0k04deAW+Co3BO4jMRNI6xeqGOMZwNwHvC3u6xEF6SirqMAwz7dIQhVqtu1AZeYw9PcswqiUFcisXo4kVKJuZhYSEpKBEZcDWcNNf18n7oNI7TE2lHvAE9TtIqY925xsf93Za4pMJY+7yqWhpVjgOeIbGpa682fX+YcYX68cFsw/vr9dU4REVOL4mkeB179Ev9IV+HOSqyUaP/0k/Es8a70kfRK2ViagrAboneFHj7zBqEIq5q1chUJ/P+yz16H4els3zF+9HgWedSZAXucbj5tOOx2ZpbVIWrENg+A3i0F8bfNNPd/kso80/7jgMYSLeDwjjF8z1UF56WZ4KsMU+wcW26T98oihphqMZ+3aeS4M3KhPTlqTb+5OGN/bLNrbdgZjHX982DOlbADI1ynluSpcnD9/DDb4xvf8gwaZfR3pvu0fhgw8bOBy8SFDTJsb/hxO6Jfd5BBh7hcGoZwFw9YEG4KvEVaiRcOfs9Grtpu5OgwiTYVLcOH/woMHY+2OirDKHFZwO3vh2+vxdwL/gLNnKfIMbP7+4WPl/rJnuUf8Y5fBAT92AYNj+7pgSxznPj978gCMKMg0b87/8nHgbPze2SnmGMfn1nmGII/RFkNJLlYw+OIxwy7ojO+TbXppu88E5nP5y8OHmf1m+8VHwueX+9iGT86FIeeiAP97VxbSeRy2ixUvfBXolc/Xb7S5VxccNNgcV7kd/vo6DM/2Y31pQ9WYuzKKx3KeJMC5D1zUtq1pIj1Oe6xy30Z+ZgouP2K4eR1xQWtX8Oz7Sw8fiszkhCbP7Oe+4ABwHqf5e4RnPkd63vjzFelMZt42/1C1garzOfrpwYPMIviuLO6xt/XFhw4xx7dIFR900dQh+G4Db79xpcb5Bw4y87bsz1s87DOgG846oL+Z88CfjbF9AkEm8b/t72fOT9iXFUZB/J3/i8OHhs625WuP7XL4e4gDfqOxrwu+J2vJmbp8jnj7DNQn9u9mTmzJSkkwYVYs2Kecv2P4c8vXQ//uDa1g7e+Rq44aYdqJ9u0W/4UVbsOlhw01ZzpzIags+J7S/kw6q1nY8ouvzZE9G56blh4veBs8acRWs1xyyBAs3FQStfpzVzGgZFVgZpIXi2aFt7FsTzzBhccA/l61Z3B3JfZ3JOdSxPt1zd8d/J3KY0trHDmqwLx2naEqf+9ffsQw04rV2a5wVxw9uqcJc/i+s7Pie75ovyMZmPC5sO+DYsFOC/ydz0X4tsDKCAYu7M7AeYc7gy1z+b7+rAMGmL8B2yr0ZQcFe/JQR61wca8P2AVy21KO+4q/G/k7n28N+bc83xvw9xZf83v3b/lrnr9f+L55WH5mxPebvL8rjhhuZsQ63zuxA8BFUwebkyTjXfHZVrit0d5f8PPLjhgadtIq8WeDnTAYSPE9AX9H2PaCDKC4zMH5Sx21KvK4sT3N+5fWHg/jjdt4yaFDQ3PyYsGT1fjzPM71nl728MClZ8+eWLZsGQYODO9/+Mknn2Dw4Mb9QaXrcLaLamqGi3PBv/nAJfptuCs37Bn5TbHzWni2iw1c3C3F3Jjo2z9KGQjYxQq77XYWTLWrwsa97c6FezujYHcIza1J8KKuuq7NWorZwWL8Y98OT7TsYrCbDaX4B4QNA5yVQpGeAy40E99I842kuxqJf5xzMZ9nSlpchLOLizxbgwvRLC0vcwxD42K/fQ3YReVI+8b5GuUiox106bycbVqyit7HkbWzgMLXgGVLgC+qsH+3vbB/5vBQG5ktfY7EDZvGmDeQvFku9KdufROH+LYDqVs4yRcomYYTun+FMzbdij4rVgANXV4Ab6IJVRrh17NGAqUrcG5tGc5NAnZu6oVyfzrSErcje8v2wGneNAvwz+6G66p9SEksR0p9OfBW45vcO3USFnh+iALshcGp36K0Yjn6ffImULMGx/l9OCExuB1cT1gH3Ma/ZfhU2fcRwTmPWPtv4OCXsbU4sHBtz9LnWc8M19xnGnZP9psuXE52lpL9GeI+jzSvJBS4OM6Y5xnU/Ah8YzWwbSaw/VtM8ddiUlYhPvctxQTvJ+hWuwWI/LJ18QC9joVn/z8hK60PPp230ZwVtE9Od1xy4BAsWluEx5cuNa/HaePG4qi6ery05hssqg9UR/XJToXHFzhznD25neyiLl/r7st4NrXFM7VCgUtwkZd/GDDk4Nd5FjtfY3Z/NFdBwDfXbNsWqRWD8/XPxX67IJuXEX7mn/N6tnVDU2dJ20Uve4Y475PbH20wpxsXN7nAzjOJ2RqOvxN4XOHQ12gCf1QUhHoSLy8sM8cSHsP5uuQiZnP4x6Fz8Z3Hv+PHN/S8ZhhjA5e2/mOXf6C7XxcWF9D488Az16z+wfCJzwWDU4ZT9jGErhPcRg5JDVSlBdoLcXGci/3RnrvmWvA4Hztb0TnbMTqDKLuNu4KLFDzW29deU1UFXPC3f0DW1NchOwkYnp+JZVvKoj5XDJuaaz3DSh7bPibSbbTFH0kMU5rD57i5/vs8Jjl/T7rxWBIKXBzPkXnNx9Dbv6lwtLnX9aFRAi0GVYeP3LWAMlbcd9Hug5fxJIho3MeMWOfJ7errgov5LXldNHWc5+vVGbjYgGx34oKoPYucixH8ObXvDZ2/P3iSCD9aw/3YeFziRzzwd1NNTQ0WoWNpydySPY39Hbk7MLBsi9CS73Mihf2x/A6OBd+/RDuZoDNp6lgY63tKiwvrbblPnLMh2RKZf2byWMyAiL9f2vI1adr1dksz70H5nponKnY0zhN+iCeIWHyf6G7dbX83t+Y1z/3S3LwovueJ1JLYOW+ps2jq/QV/HqL9TPDvSH44udd3OiLzM9uK96i7U1MnqkXiDL+kCwUuF154IS6//HI89dRT5odww4YN+Pzzz3HNNdfglltuic9WSqfgrDBpMnBxrGk3V2wRLRuI2FKsBRUuPJMiFLjUNR24OIMcDtC2gYu9LdNPv6a+UXWEe4YLZ4q0pMKFLZ94hvq5kwfg6U9XmdZIXJR49L2lgSqKnDRzdqV9HOzD/+j7S00a7jw42+1K8nlZd9DsPo+Ei1rvLtxszvQIDL1tqHCJlNTbnpNOrEioqmn8PaxwsUora/HK7PXmLNxfHDbU/BK1wQ7P3LbDmYuDLd+sB95abN608rl5/IPlof7fXMhhVQQXirmAzNsP3FZC2EK//e/X527EzBXb8cvDh4bKu50D4yIFLmn1hbg+8WLkzlrVeMdt/zrwEZSH53B45iP4tmQgsjzb8PPEG5H9hWsQ9DdXwTTLinQyTaSwxX69KNBn38r2b4R52xThhBRPzQ7kNHOiSveKr3FZ4tfAesdvi2BO6OPcgFhVFgIzpuBiJKM6MQlJO1OxMykRpTWZ2PHhFOQOOBhIH2QGGSOxALX1jTfM/rw4f4Y+XrrFtEDh3AGeicaKJRvE5aRGOGu4eCnwwbEmlLL4Sj7U/fLNHgMkZgfnK6QCyT2A5FyAA/hS+wIFhwLp/UNXt2/Q12wvwzOfrsQ3a4rCFpX5GuYip22h5G7FtSvsfdrFccuGHC99s95UENmf1V0ZcuicV2J/jrjPbbtF5x9JxJZA1qhezS/a8Q9BZ9UF91FLzyDjNjJwsfuW+zyWM7Bs4MAz6YgLftHOvm8p+7y31x+7fK7Ye5lBszMAyA0LXBqeu/zM8DMi22pfOP9w5fPkfF64EGVPVGhNKDWqZ1ZogZa/H4cXRK8qiGTKkO6hwGVA911bzHVX63Rm3H5W/TGw7opnwHc1dvGKP5u7o6Il1tegrdJi9Z2ISGfmnA1pT+7j8S1e1QI8rjNwYaeFjliV4W75ZGe6iIjsqVocuNxwww2or6/HEUccgfLyctNeLDk52QQuv/zlL+OzldIpOFtqVTXRUszZaitay7BooUXD9zUOY+xCYFPs/ZkZLsE3O6xq4P04K1CcbLBAtg2TszKDZyBFmlkT9jjr/WFVGc0FLgwZ2OPbzjqYyaG5Ho9pXWMXNtkfmL2v7R/NSzaXmLMViytqwwIXW7WRaOeU7ELi8uSHy82//5u3MTQPxQYh6RHanERqj8ywYnlw27lIZhdbbfWCxfkjtHhziTnTwhns2HYxzn1p9yffzDJUsnM5eOYW2zrc+foCs18KSypD4QnPpHSyrwUuSPKDrQDsWTY2pPGgDtsKlwNZM4HFvwNKl5sWXHdzIrr78WaNCCQmxcFp6Q5nVf0SZ8XYRWR1/QhsRW+kecsxqocHqCsDMofh+ZX9zeyPcVmbcUzPNcC2L4HiRfCnFOC78gFIQykKPGuRgCqUIwub/f2w059rBi/3Tt6BHt4tqKiqRK03A2X1qSis74NN6G9ahKWiFMM8c9DH6yytabDF3wsejw/L60abbevu3YohGSVYVpyGhNyJWFU7GIu3s0KjL8ZtuD4UOCWiComeKqCmxLQ2y+XfARsXARufCt12Qmo/XJqRhzpfKj6pPwnf+g8LC1qcPzds1fb3z1eb/tNs08ZBxEXBVnNZwZZiRn2d2Tf45PSwsMWtzNcT6UdNB7pPREvYRVZWlfDDsvM13DMr2uKPC7bF4M8C+2+7F8fZeoKBiz3DbVcDFy688ywotvph0Pq7d5eGjrH82XK31jt6TIFpZXXyxD4x/SHJPwTZHob9mW0bq5biAjuPf3bfOvd5S/7gG5zbsoX6pjCQZQjPbWmPP3a57/fqm4NZq7aH7VNnyGKHlLrPiGzL0ICvOVbIMKjjz6YTX7t87ldtK2txSOLE1ioMWlnhxH9buoAxqX8OXp+32YQ/vXN2LRzjWfd8rGzL0prhyB0BW2m8MntDh2/LIG2DP4MMW1mV01T7xt1dgcFKcrY66qjtS0REWoInQvFvVPu3q7tCvC3x9/f7iwo77O9xtsHkCY3sTsFjvJ3dJSKyp2px4MKD480334xrr73WtBYrLS3F6NGjkZGRgYqKCqSmRm9XIHsuBhbO0KHpCpfYW4pFu5Qhgvt7bSuMptiqFC5E2iHZdfX1ZlE/2rZUONpQVQSvx++3lRlcPNqJxgGKM0fgkHFnQNRc4MJZGRYH7dl9/NnybVFDLtsui/23Iz1mW7HRXMjV+HFEfr4agpDGh5FIfygzbGFwRJyLwD+qa+vqzH7k9d3hGu+LX7OVS6alWHBRwC7A8w3l0WN64r43F5kKGjvbhe0cfrR/f7MQZofufrZsWyicynAMISfnLASqsu3hytehYNsL+JnvLYzxzkQ622+4ClKs+uR8ePf6DdDrGCAtOIeGw7H5Qd9cDax7ufH3ZY+Dd/ilgSoKzmXZ/L6ZoVLf+3jUpRyJx6YvNtd75LCJZh9wjsaM5YFqllJfGo6ZPCZ4QzXYVFyD3778HZqS603GyF6ZpgLihAm9cMjwfNSXV5sQxNyPWVivwbHpM3Bk9e+QmJKDjOFnoQQ98LdlvfHNTtfclTrggG49MHPHNhyb19MM8ly1bTs2+Pph3OHvAoseQm3h59i4aSUSPdUoSPeguqoUCbVF8HnCX6ueirUYlxSYx7CX71MU5xyGj7f1R1HJBPgruoV+bhiGcYGYMyJ8qEFCXRW+WboMdeUb0Q0V6Fm+AZjzFbD1U2DbLKDW0a82tTcw7nYgKSdQyZLUHaWVlUjN3xdIbHkYwjkydoCpXTDiYExndUN4pUHr/7jg/d17+vhQiOoOE/h65s8Pg8NdDVyIgSWPeTzj3Z6hZ+/DjZczZGjJTAJW6DE4Y+DZr3vL3zvwuHHDcSNN32WGrc5qn6a4n4O2HCDMfXP/GRPM9rSX8w4ciB/s1y+sSsH5nLlbOrAapSFwaV3bHqdrjxlhfv+4A27izCEe+yNd1hIMWvYe0A09diHsSE704Y6Tx8ADvh/YteeL4eOvTx1rqjo7+wIxe+E/9IMJSIsyB0f2LAwI7zt9QsTfI+2Fc4LYxq+p1pAiIp0J//bkCSj2hMBc1wzEtsTj50Pf38u0MeuI+D6Jj5+z77qnJzZqKy0isqfZ5aNxUlKSCVqoqqoKDz30EO677z5s2rSpLbdPOuH8luZnuDj+u5nF/2jVGM4ZLnxTwSqEmAKXYECR4PWE2l7xS00FIM4KFxtscCGLQQ3ZNlduzgDBDmp3fs5qi/Tg/BM3O8iZ1kb5b+dwUbOdwVkn7tZqdcHr2MClpTNcbHsuci6ONcxWafz4I72Bemv+JrNtXJDlmdd8Duxj4G1wILgT9w/DI7u5rO6xFS72+eLnw4JDubm/7YB6Lt7aRWa2s2HgwuDqhOCshYzk8D/muS0DPAtwhu8R9PSsgW/2GPi/XAlP+WocYh5Q4/3i55B6zjyor0KRPxcJU99BRt648CsldQt80AFPAx9tN0PvV2MMyivKsc3fC/sd9gyS0hw97sfcaP7hs8WpWBxyy8qCrSXVyEipx4yFgUVvCgsJvYnYWhr++ohke3l16E0/gwAuujjPzOZ+4/3NqDgGb9YfjfP2G2h637K5R8L25cDOwLwCp5XbArfHbbXbZJ6jpJ7A+DuwbmuZqTTibT90yl74cukW/PPTBTgybzlOG1pmKk/qdsyHd+tH8NQ3zDfiXJzjue/5pZeuwv2+Hqj1JSGzMhXbEutN1UwPbILX4wfmAAeZfQ7g2ygPnqHWUR8H2pc5tLa+gfvRBi4cPOiek+CcWWHbfLVWtIVqhi1cfGZlgf2Z2tXAhWc92zOf7Rl60UIj/gy2JGyxx6TW9uUPVPq07Bl0nlHH/dXWrWtaGyK0FverPd5HDFxcr8F4tcWKNsCeWJnVFrvJ/PHeip+ppgbR787b6CjUSqxrae9jVaSf585eKSYi4sa/Re3fXm3RWrgpzjmWHREfPwOX1rx3ExHpLGJ+p81Q5fbbb8eMGTNM2HLdddfhlFNOwdNPP20qXnw+H6688sr4bq3sEYFLXVtUuNT7Q2FMQVYKSitLTeASGPob/WwJm0+YlmLBBX+2BmsqcHFvI8MdLkrYypHUpPCFLd6u2T5n4OK6fZ5NfMXz3+KkvfrgpAm9mwxcoi1kMbyoqXVUnASDFn7duR9sFYytDmlh3hK2Lc7qmYqahtkqbpFOWLFBENvLcJHTnlHM/Wjbszkx0LFhFxcPufCbkhj8nmB1Edth8LbYQoohlt1W5wLzxH7ZSE/yo6SsBGuXr8RR3g+xd9la4J0tQOkqwJuA71f60C1xacOdF33QaHvK/RlY4t8LA/sMxPRte+Hb+sNxxyljcOs/30YZsvBA1qimdyQrKo780Pxn8bqdePidJea/J6c0PUCNb0gZgHy2fGtowdtyv+abCh25Txhi8bVpg6lIZ7Pbs6Ls6965aOp+c2zDIDsbwtlmytn2zW6XPbOe21KFNMyt2x+njRhjBr0/9N0SHD0sDW9+9iWOzZuF0xOfhMcfHh7meIIVXpVA71hPikrtA+TuD/TYDxh0LpDaMHy+rXCRes7aItOXOVJbK+fMCmc7p3jhdjBwacs/vpxn6HX2P5KcFW37DOjW6SsTYmFDMv5+ynEFcM75JZGGhoqIiIjIrtl/cHfT/pQnGebFscKlM7B/C3b2vyVERNo0cLn11lvx5JNP4sgjj8Rnn32GM844Az/5yU8wc+ZMU93Czxm6SNcUasEUVF1XF1PVSnPjRKJVwPDrNrjh0N/lhaWmpRkXlNniJxpbleJzVLjwS8VVzQ+xt+wcELvo765wYauyQOCCRoELB/vaOQ/c/NlriiIHLsGKgUi4AM5heHzMzv1sW5/xdhmA8Xp8Yzd/fXHYIL3mQq7G21IesdqnqQoXu2+t4T0zTSssLrQfNiIwX8aegc2qo2pHcGSVVtWEqnZsqOOeV2Hb9vB2GbjYRf7uieXAymeBdS8haeNb+D3KApUPRcGjHnevYxdHijyq/MlY6R+L5djLBAPbU/bG9op6FBSlYHMpA4ZarNxWgR0oMNdvSVk0K3z45puVNs19HysiVm4tw8fB1nIMnlgVsHhTiQk22a7OnpW8JULgwrZrlbV1mNivG95dtDn0+uMb3UiVCu7qH1YWNWxL+PWHFWSaIcsN35sQejw7IwQutu2PDcRsa7gH3gq0TZu+qARbantget05OPrEG5FVuxaPvfkZBmEuDsxaiLrS1Uj01oF/q9TWVqOmzoOErCHYUJmOoko/6uGD35uIvYeNBPIOBPIOAtJdLdDiYMrQHpi3fieOHRu57zx/Fg8fWYDNxZXmZzfeAn/ElJj/5vZkttFZzGyV9sJXazFliG1A13mx5eA3a3bgh/v3R1fAUHB07ywzr8XdQpGt2PYb1N387uxoZ7yLiIiIdGashD1lYh/MXVdk5pN2ZZMHdzdrGG3ZzldEpKOK+S/rF198EX/7299w0kknYf78+Rg/fjxqa2sxZ86cLnF2qDTNPTQ+0hB5ajynI3olTOD6iBq42NyAlRs8g5sL7ltLq5sMXGxVSoLXG2ppxeCmuZkqdqHbLHAHB0fb22KVhRNvuwr1YcGGvX2GHs7B2uuLyk3g4Owfz5BkQ7BiwJ4Vb/+lvt1TkegNXN8ZVNgAxFaTcJGXc1LYVoitfsb1yTaD9Fo6w8VZ4eIMXDjbgVITI81wCa+CuP7YkY2uY/uGMxxyvy6otKouNJcmK7EGWPIYJq7/EN18JVjqnwgfajG+qA6Yk4Lv1SzGGl8aNvgHo5dnJUZ98R+gzjG3I5rkHkBdNfy1pVhVPxIz64/F+/WnY9owL6YvrUcdGl5LZ47ri+e/XBOq5iAGIZZ9PcWCC54XTR0S03XtkPXK4P7+/j79cNjIfFz9whwzJH5rSVUocNlW2tCOyyrITjHfQ3PWFYVefwcOy4147HbPt3FWuDgHvvNbR7gCFz7XtgWV82dqS3C77PeHApeKmtCge8u+EpIzesGT2BerU9PwTelUbCvIxwc7Cs2i8dVHjzC/vOwrb/uaHXj0vWWhYe977+tq7RZn+ZkpuOWEQIvNphb4dxfn88Tqr7b6Hc15SLedGJwZ1MkdMarAfHQV/B3Dn5tox6OfHRLb8UhEREREWoYnZfGjqxuan4nbT9oz/pYQEWmzwGXdunWYNGmS+e+xY8ciOTnZtBBT2CIRW4o5J8Y7uKsrolwtJNJCvP0+WynDxSK26QkELlVNzgSw9+9sKcbQx9n+qKlB1Vxst3MRQjNcXH3q7cJ72AwXW+GS2TBI21bJbNxZGRrmTut3VJjHxvZMBVnJWLGlzFQSrN5WZoIstn+x7YKcM1zC233VIQcwg9FtGy9W3gS2CzHjYwgLXBxzVmz4ku5uKVayDN2+uw/jPKMxz38g0njGdNU2wJcGJATP7i+ah33qXkFfrx/VpZHnJ7B1G+8jFSW4oPpa4KtvwFqg3j5gCt4IXIkPbyswlscl52Y4C6yS81CUMBDbSipQ6O+HFf7RGDl+GiaN4ZD0TLND/jFzBd5b3DCbZGNNd9QhfFbJvgO74e3vNmF7cFYHrdzSELjEa/Cfu6rEtvxhdQrDiiWbS7CpuNIMLQ+17spIDv13tPkdB0Y5s8g9aJFDoS1n+TfP1hqYm9aoOiYlMfCaLHLMLWIo5Px+hqI2RHz7u4Y2aRxebf+1LfC4/QySbNVXpMfDMDErNdH8HO/qvJI9ibM/tPaHiIiIiIiIiEgHDFzq6urM7JbQNyYkICOjtaOGZY9tKRZlhot7YHtzA9yjdb8yLcVseOLxmEXpZYUNC7vNV7g4A5eG1kYMVezwa7cewcAllpZi7m3fXhbYLnfvfFq7vTwscNkQHFLPNi+c/cDAhSESb5VtpLjIbQMcZ+BSHqwGsdUn/Py7DTtDA69tUBAKvbjvCz8A1r0KlCwFMgYBI64AMhvOdOb92EoWcx+OUMfeX1hLsbpK4IPjkV6yBFckAuvrB8NXlQT8ZxGQ1hc4/F1g5d+B736Nk8zOAuq/uQtXJExENVJQ509Eoif4HG7rjR7+Xrgt8Xnk1W1ES/g9XngGXwAMOgfInYzV60vw+3cbZrSM7DY0ELaQxwOfL/x5sa8HJ4YE3I+vzdlgnmM+/3ZYPIPneIXPzpkfvAu+LszXM5NNi7gXv1pnPufztDH42hmanxEKXJyvOb6OFmwoNlVZ0Ya3p7sGQDtbinGYrQ1KGMzw9Rn2vck+eD2B72cwydcPF/xtqzMbHvFnj62LWC324ZItoe/3B+tbeJ92f9rAwAZ/DFYinb3P0vS35m9qs6H0nZmzP7QCFxERERERERGRDhi48Ez38847z1S2UGVlJS6++GKkp4dXE/z3v/9t+62UTlPhYttuRQtc3PlKtAqW0OWhBkPhWAFiwxrOC7Fnzm+NEpY0VeHCge22WqNHE4EL23LZyovA9wUDF9cME1+w3ZezdZcdUs75DTdOG2WqVTYXV+HdhZvNQvKUCPuSFS4nTOhlFraPGJmPoooazFtXhP0H9TCL5u7AxdlSjBUua7dXmP3NEKkgKyVUcWC2q7IQ+PBEYNuX4Q9y6ePAvo8BQy9q1ELMXeFi232x0iGwc6uAubcAJYFh8ObxelcAdhPL1wGvN25p4/VXYZx3ZuMdzjV65iw2x0jOw+ohD+GlOVsx1DsHO/25mDBqb4zt2wNfbvDgy/mz0cOzCYmJSTh92rlA9shGA/qcra/CtsEVlrhbzLHKg6+X48b1NBVVg3qkm6H3rKpqaTuxlnJWlbBdlp1j4658YfDC14MZ3N47CzNXbGsUUEwb18uEEwcNjT6Dw13hkuJomce5O9mpgcoazgVhdRf3jQ3l0pMSzP4ZnJdugsLPl2/DoSPyUFgcaMPmDGj488TAhe3v3FKSGu4zO/hzZwPOSKElcRYSwz/OoujqnK8ZBS4iIiIiIiIiIh0wcDn33HPDPv/xj3+M3WXVqlW488478d5772HTpk3o3bu3uf+bb745rOpm7ty5uPTSSzFr1izk5eXhl7/8Ja677rrdtp1dmZ3ZwoVstv9xtxiL3lJs1ypcGLaEtRQLLqjHXOHiawhc2BnMtuNiOBFN9/TAwiWHlJvbCoYdKcHWR+7ZJDZwYSWIbQHGShae2c8KhE+DQ9BXO1p2BW63oQqHszm4kGwDmD45geqGHv41GORZieq6PhErXLiIvb0ssMg9gNUzfj/Siz/HJM+3SK9NBd5+BCgNzLwI468DvvwZsPBBIGskMj098CNfKaqRjFJ/DsprusFflAxP1gikVa1AH0858gq/ApbOADbNAGpjmJtCHi/mJZ+BzWV+HJI4HYn1TX/fhtSD0fuIv6C6oifmzV6EeXUHmq8P7TEY6NUDvurt+NafYwaA9EtPw+mOsMXsL9fz6h4M7W4HZuf0uAMahh18PhgUMmizc1V8wec8HlhV4hx8HWlR3Rm+sQonJy0x4oI7AxL7eoomMzl8gd5dwZWbGQhc7D7kvl1XHQgU7TBubgMDl0+WbcHQ/HQT/HHOknNbslzbxXC1orq+UVWNOzCIFiDwNk4Y3/Rj6yq4j2wVlgIXEREREREREZEOGLg8/fTTaC+LFi1CfX09nnzySQwdOhTz58/HhRdeiLKyMjzwwAPmOsXFxTj66KNx5JFH4oknnsC8efNw/vnnIycnBxddFDhbX+LfUoxtl2zgwkVpd5ulRi3FmglcolXAMGyx38p583ZmgW2jFI2du2IqXDwNFS72DP3uwbPpI+kWrcKl0QyXYIVLMHNipYm57fSksIV+u3i+vLAUD7y1GGdPHmAqUWyQw0qEiDa8iZPWnYRTE2vw1db7Af/VYdUovbACyVtXY235Xg0zP+bfib7zbsPPufbKogxbwJFSAOz9MNB9b2Dxw4EKF2KVSskSsOnWEa4RLZh+p/nnJv4fb++7xptYPuIW3D9/ODKxHX2GHIQfTOwBzDgYKF8L9NgPmPggPvquJ74p3oFNQ3+DLxevQE6yH786cTi2VXrwm9fnI9e3FVP6VOGDtWkYN/JQnJbVF8k14eFUcnDfOxeVIy0w8zliaGKDFHcVhztwcVbyRApo+Lrm87dkU0ng++M4y4pVYwwrWE3Tv3t6xCDGXs7NYNjhrBpxhi+xcO4bth6zIYqzxdmyzaUmAAxsR3KogstilcnzX67FxqJKfLB4S6OwyP08MYhkZVlFMDRMaSpwaeHj6Yr4+mRrNbZAbOnzLyIiIiIiIiIiuyFwaU/HHnus+bAGDx6MxYsX4/HHHw8FLs899xyqq6vx1FNPmaqXMWPGYPbs2XjooYcUuOwGtqKFFRk2KGGYYueZWH5X4Yuz7VYk0S5m1hHeUiyw+Mw5JZGCnsbVIw0LydwGGxhFW5zkgjzDJGcrLRsWuYfGN8xwCVzO9mGRFpx7ZaeEQoCFG4vxxcrtpvqgJni7tlImTOHHwMenwhdMTPbZfC3wn7vhT+2DIbU/xcEJr2Af73vAYqC7dxTSvUfjwG07gM1/a3xbHh9w0AtA/tTA5/v8AaivBpb/BbskORfoPQ3odzqqux2NNfPmBh53aj6Q3hc4fkGgrVjWCDOMJGHhcnM5u3KVIQfJCUlAxkCkJ9ehCIUoqstHlqcb1vh3YP9g2zJneytzl8HqIme1RLQz+lkRYgOXdNecEneo4MZh8G4je2aGAhdbYRUvbGE2u7wII3pmhoUUDERYgfKTKYNMi7MJfXPM42TgYtt9OatFYuF8PbvDRBrYI920CusdrLY6ZmwB5q4rwsheDdvGNnP7DOxmrseP5gIXXsYKncLA7kSq43m2M2vsz2HPrJQWPZ6uiq8ZBi7uOTsiIiIiIiIiItLFA5dIdu7cie7dG3r1f/7555g6dWpYi7FjjjkG9957L3bs2IFu3bpFvJ2qqirzYbFShmpqasyHBNh9EW2flFdWo95fj7Qkj/mXZq/ZjhEFGWGLtpXVgetZ1TV1Te5nVns4rx/anlp+X23gsvp6pHH4ur8elTX1KK2oirhQzCCmpi4QrPjrauFB4LarqmtRVhm4raxkb8T7S/b6kOoL3MfO8mqzzQxp+HlGotfctp034wWrb+pN5Qyvt2prqfm8T05yo8d603HD8ch7y7GuqAIl5VWB262uMdf3cHt5fX8dPOtfhnfjW/Csfg4ev2t/VW+Hp3o7rky4POzLvesX4gcJC4HNDV9bXj8Gy70H4MgB1ajvczL83SbzSW24wsTHUNXnPNz9zjqU+9NxzBAvvlixBYNzgKqyzciqW4v9vTOQ5dmBFfWjUYYsTBkzDuh9HPzd9wmEOAybKgOPgZK8wceBZCBtCFAbCD08wf1UGrwuC3p4Pa/fD+Yf3H+bisrNZUm+wG34gs+Z5fUH9nFaQsPrLiPJG/E1lZOaEHiNJiagvq4W9c7RIfWB5zKa1ERPo9s8emQuBvdINSHeoNy0uB4vzpvcD9vGF6BPdlLofvh6vPX4kUjyeU1wd9sJI9E9LTG4n4Bbpg03VVK1wf0dqxRv4HVOyb7Gj/vgId0wqEcK+uakmsuG9EjFLcePQG56w7bRAQNz8OmyQHUL9ckOf/1nJDY8Z3xcG4s88NuwMfhaoP45yebnhHN1GGylR3gupLEf7tsHR47MjXjcEenKmns/IyLSWjrOiEi86TgjIvGm40xkse6PThm4LFu2DI888kiouoU422XQoEFh1ysoKAhdFi1wufvuu3HHHXc0+vrbb7+NtDSdGew2Y8aMiF+fVehB4XYPltdsxpYdXEoHbnuhEIMz/TiyT0OZSkkNUFjYcPb6wvLNmF66MOy22FGLC+4sUlm71ovSCOvFnpJCFCb5UbjTg9mzNwNr/dix1QuOkvnv628hJ0LBAddy7X2/+84MLNnsQWGxB7O+2oxVWz1g8cvcrzaFbZ/Fxe0vP1tvLtu5FXj5tVVYsipwvc8+2YSd27yoDC7gJ5UWorAcKPUB06evwccrvdheBaxduAnT133b6LYTdnhQsrUCJbVv4MNN+fhma1+zLxfUbsb0wtmYWP0o+te+F/Y9W+r7oodnPbyexiVA9X5PxK8v90zFZeuvRorPg4r0evi3AP7Z082+tlioxOdo9uZA6yp/ZR2WFvfAhlKguLoHimtG4684JnR9zjbfkVUPrGUVw1uhr3Nf2P04f85m+Nc03p4lmzwoLPLgOz6XFUDtTu6vVeayoq1elNcC27awkgmY+/Um0+WMz6/z+eG+XxR8rvk9HAGyFJsxfePsRve3MfgazUrk/awLu2zONg8Kt0SvcllRsxnTiyL0Tgtag91jzm64DxZmbS30glGIv7jhOXGbH8PtVO7wojj4u2jJN5uwyfFNy4sbnstlczZhzXYPthQHnoPVy5dievmSRre5dpcfVdcVqDMTFEIvcwAAUhtJREFUkVjfz4iItBUdZ0Qk3nScEZF403EmXHl5+KiDDhm43HDDDaYCpSkLFy7EyJENA7DXr19v2oudccYZZo5La91444246qqrwipc+vXrZ+bBZGVltfr296QEjz9kRx11FBITG7dXKuK8hiVbsM+4Xti5sBCVwRZdHIU+bdreoesVllTh3ZKGheuhfbIx7bAhDbdTXoNbX12ASQNycO7kAfjwv/Oxo7y60f0Nyk1HfmYyilZuxz5798HRowvwWc0CFJZUYp8pwzC8oKG9kbPt2as7Agvx046dgOKv1mHn8m0YO74XVs7daL5+8rFj8flLjZeSOSPmpOOG48N/zzOfv7HTg/z8QIhw5BGjseKjlaZKhUb2yUb1+p1m+PiRR4/Bq/+ag3y/H2eeMDZs7oZRvAD7fXkncpNfQ6KnGv6qFFQNfQElq5JxwsCVmFz/b3g3N4QtftblDL0E36ZehzXf/BmnpzyLzMEnomrrHCRv/xSf1R+Lf9ZegVpPGgZiHsblFOG4sQVA5nAkesci//WFpp3WtGnj8cDbS7GzsgY3HzfCVAS9+PU6fLBkK86Y1Af5ZYGl7b69srBzYzHG9svBtrJqrN5e3mi/TJs2ptH+Kq+uxZs7A0u9B08eiAMGN1SjWSVfrcO2RYXolZOK+qIK0wJp2nEjzGVfvb4wtD/p8KnDMKpXpqkkml40O1RNdOxRY0LD4z+vXYDNxZU4/OBB2GdA44A1Y+lWbPhiDQbnpmPasYH7sZIXFmLl1+EhjNPkCb0xbVxPdBUfVMwzr41RPbMw7cihu3w7/rkb8ercjaaq6Acnjwtr9bdkcwnmzVhq2vv96OTxSP52A3Yu2IwtW7dgr3GjMW2/AW30aEREYn8/IyLSWjrOiEi86TgjIvGm40xktjNWhw5crr76apx33nlNXofzWqwNGzbgsMMOw5QpU/DHP/4x7Ho9e/bE5s2O3klA6HNeFk1ycrL5cOOLSS8oxLxfWHng9XiRkZJkBplXB2el2O+xfL46c70QjxcJCQmhhdjNpeXme1duqwh8n4fVGo0rTvzwmu/lZUnBbeqekYytpdUoq0HkbfTXhm4rJTkJSYkJ5nOO9bBfz81Kg8/LFmGBYeXVwdk0KUk+ZKenhm0LZ8dwnkWvnHR0S0/Ghp2B1nT2drnthWUszwnMf8nPTgufLbPyWWDmT9DLX8urBHZHfSWO33gSjudLMpABhfYTxv8ankFnw5fWF6lLt+Bz/0ko63EOLt93GFZvLMbv3pqDGqSEbms59sbAXgVIGNo/sF07K0Pb5fH6sGxLYLbMpyuKcOzYnlhSWG6qi75eUxx6nKXV9ea/U5MTkVHHx1wZ/npI8Ebc18kIPDeUkZoU8TppyYnmOuU1gftITkoIXS8rLQne4P4MfJ4cuozPRRVfcLyNlIavTxmah8+Xb8XoPt0i3t+E/t3x9oIt2H9wbqPLk5MC2+LWKyfF3Be/tysdD7j/S6rqkJ7SuuPg4aN7Yc76Eozvmx3W7pGGFGSjf/d0DC3IRGpKsnmOPcFyq/SUyK8ZEZG2ovd5IhJvOs6ISLzpOCMi8abjTLhY90VMgcurr74a8x2fdNJJMV83Ly/PfMSClS0MWyZNmoSnn34aXm/44ujkyZNx8803mwTOPngmcSNGjIjaTkzaDod026HmdjC5e+A12UH31vz1O3HpP77Bjw8YgClDckOD6DlAm1xXD+FA+uBVzewPygoOteesh0hq7TcEh2/zg8qqAtvLgIVfY7VHRXWdGfxdXRuorkn0BS5zuvn40abSxty3YwC4HXbPbVwTrAgZ0MMRtvBBLXkE+PoKEx1RhT8dqZ5AANJIWj9g0u+Afqc67iPwoKuDM2nKq+sCYYtLbkbDIrfdfG4Xq32sr1ZtN4FLaXA/rNrasB3FlTWh4fQcwO62vaxx9RE5c6VI83ScXy+uqA17TJGG1Kc5B7knNAQu3C7rpAm9zUc0rIS59/TxES/zup5b69Dh+ThydKA1YVeSnpzQ5HMXq+zURNx+UuMKKHvbd5w8NvR5ZvA+KbWV9ysiIiIiIiIiItIeYgpcTjnllJhujAvKdcEF4LbEsOXQQw/FgAEDzNyWLVsaBjHb6pUf/ehHZhbLBRdcgOuvvx7z58/H7373O/z2t79t8+2RxiqCgYt7odSRcQQ+d3+Brb5q6rFgQ7EJXDgonWqCFTJsIRU9cAkOqQ+u7nNxt6nApS54m1xc52vVF/w+GzTYbbeBS3qyD0XBDlocTO42sEfDjJ+ctECw4UEdBlZ9hErPdmT7S1GwZAXO9pWgh38osH4qsPEtYMtnwI5vQt+7s8/5uGHluRiSvhXX1P8YqKtAiT8b2/N/gAGjTwJ6HQN4w39UbTjBge2rt5VheSGbtzXWI9huyz5us+/qGwIyWrm1DJt2VoaCJxt2kQ3PkhN8SE9q/Fzw/iOxz4ndn5HY/W2f40RH6JGREv54GX5ZrKBC8Dl2Bi6tYV8Lbsmc3t4FZQTDj90ZfDif89YGPSIiIiIiIiIiIh02cKkPLoK3F1aqLFu2zHz07ds37DK7WJudnW0G3V966aWmCiY3Nxe33norLrroonba6q6lMlhx4K6CsAGKZUMSt7KqurAFfLvoHyGfMVgJY6thbOVJTlozgUvwvhOC17cBhA1c2KrKLjbvKKtGpmMBmNUvNujgth0yIi+sPVhW8Lon+/6Eo7c8haNtgUYxMJI3y5nyH97feKPG3IyKATejeuV8rKoZCBz/Dd769C28Wjgep/cfgwF98iM+FhsAsQLlV68tCFu0LnVUGOU5A5fg9rorXGjmim2h9mmRArLA4298uMjPatyOzx1g2CDMzb2ozvZkkb6Hz5Nz4T/Z8VyEtWhrBffrNNo2dhV2/zN03N0hj62UExERERERERER6WzadYZLrDjnpblZLzR+/Hh8/PHHu2WbJHqFyzXHjMArszdg6eaSUKWKZUOSSEPWnW2/GgKX6BUu9rZirXCxYY4NaGzwYis77KL+6ZP6YsHGYqQl+bBoY0nwuoEF4GuOGY6563bihPHhratY4ZKOnTjK+0/EJDkX2PshYNDZSAu27WLViT9rBBYmeVGJnUh0tc1zsgHQlpLwll7d05PCApfczMgtxZwVLrR6W7CUp4n7Swi2SqNrjx2Br1btwOEjIwdCDEnOnTLQhDjcpkjc4ZyzpdhBQ3OxrbTKtErj/A9nOzcbgrRl9Yk7gArdV0LXDFyOHFVg2sIdPCy2lo9tHbiopZiIiIiIiIiIiHSZwKWsrAwffvgh1qxZg+rq8AXfyy67rK22TTqRymo7w8WHft3TTHXIba98h1pHe6qmKlZslYkNXBiOsHopytVN2BK1pVh5dUwVLj53hUtwAX9sn2zz8fZ3mxoFHEPzM81HGH89eni34HjfM0jxVIS+/FLtz7DUPwG1vizcMGkzvKXLgbyDgILDgOQ8wOtztdYKzGKxwZAz4HCzl7lbrvVIT8KaYHjCQMPZistW9PBbbEWStd72TouCVSXONmGDczMwsmdWk98zdXjTi/XuRXU7+4YY0vzkwEFRt8X5bzwDl67aUqxndgrO2n/Abr3P8AoXBS4iIiIiIiIiItL5tDhw+fbbbzFt2jSUl5eb4KV79+7YunUr0tLSkJ+fr8BlD1ZYATz92Wr8YN8ByA6273JXuNiFUlutUONKWKK3FAuEHnWO1k6sjok+w6Xhtmxw0pIZLs7v47yWyAGAN2IYEGbhg8DcWzGkrhxDgt/uhxc3VP8HW9HHfD64Wzq8o0ZH/v7g/dhWZQxcaoL7IOp9RpkpQ9WOiiIOiXdyBiYVNQ1VMLStNHJIZXGGiw15uJ9tANUa7v0d7TG52ddYW2yD5az44evCVk911QqX9pAeVuHSNYMuERERERERERHp3Fq8qnXllVfixBNPxI4dO5CamoqZM2di9erVZm4KB9rLnuuV1V58vmIb/vzJirCvc3Hazv+wbaJsFUmNq3IgWkuxsuo6E644h7AzgIiSt5jZIrZ4xq7T2xCIFSuR7scGGXbb3Av87rPqnRUmERf3t38DzL4OqAuvDlmTdWoobKG+3dLQnLTgrIwKZ4VLDC3F3KYM6RH6bzvTxnJ05TLBTksqOHi9/MxAgMMKprbgntOREHPgYitc2i4MGdM7K/QaSHa8DjRLZPfha5qVTd4m5v6IiIiIiIiIiIh0ZC1eTZw9ezauvvpqeL1e+Hw+VFVVoV+/frjvvvtw0003xWcrpUOwEcbCjcVRqwNS7EDz4L8MPpxVKlFnstQHBrnblmI2cGlqhou7pVhmcoIZos4vlwTnorjvw7mw3yMjqcmZIs7Ao1H1RX0d8OXPTDsx8ucdhHfqfoC/1d6AT3LuCLuqO/iIhPNiqKy6NtSGzTm3xM1ZfUNj+mTjumNHYr+B3SO2aHJXuNjAJT8zBbHg4x+Um47rjxuJCw6O3OqrpVIazXCJ/nidbNDSlhUuo3tl4YbjRuLu740La1XWlqGONO/qI4fhlIH1YdUuIiIiIiIiIiIinUWLVywTExNN2EJsIcY5LpSdnY21a9e2/RZKh+POQGw7MYYANsywVSTkDFEcHcMaYWVKeODCsCbydVkEYqtYbJDAsCUrNbBQW1TeOHCxt+0LXt/dciu1iQoXd8CBpY8D278K/Hf2aHgOfxf/rLsaH9afhp214bcby9n66cFZK+XVtaE2bI3u08EdNuSkJmJEz8xQu7TmAhfbRq0gKyXmChfu3+EFmchKaZvqg6ZauMVW4dJ2gQsf27DgY3Pu2646w6W95GUmIze2l6SIiIiIiIiIiEiH0+LTiCdOnIhZs2Zh2LBhOOSQQ3DrrbeaGS5///vfMXbs2PhspXRodvHe2X7JWR3CShW7mB6tYoXKq+pcM1xY4dJUS7HwGS423NhZXhNxjott1WWv7w5cGrUUc9yurdgJSesDpPQEKjcB+z4J+BqqZWx7Nec2NccOt2flia1wcQY+bu5qEFsh45SREv7j7dxPtsIlI9lnqgnsDB1W+djn0ykelR58TTjnpcQeuPjiWn0SXuGiwEVERERERERERERi0+LVxN/85jfo1auX+e+77roL3bp1wyWXXIItW7bgySefbOnNSSeS5ljfdrYJq6oNDp13LPo7A4GwCpfg90UKE0yFi2OGS+C/IycuzlZlzsqNnNRA8FEcoaVYnb3vYPDAkMLZ1qqpiotGLcX6nQqcsAiY8hyQf5D50vcm9TX74LSJfcOumpMW3rosEhuYmMDFBhBNzXBxbU+ao5rloGG5Zp7NIcPzmpjhUhsKLWxVEO07sLt5DOP75oR9b7yCh2ivmaYML8gwocuoXplx2SYb5NiqHhEREREREREREZG4VLjss88+of9mS7E333yzpTchnZSzYIIVJDZIqKiub1QhwoVqW73gDFFs4MIwo7YuvJLCzC9xhDPVdXVRW4rxdmyA4qzcaKqlmK2e8QUX9rmNeRnJWLu9PGKFi/N2kxIiLLwnZQMDfxT6dNq4XjhubM9G2xxLhYsNHhiE2P3VVIULt52X2+umObb9JwcOMmGUOyzg5/wSt89WsTBUYEi1sajSfD4sPwPnTB6AjTsrMXddkePxxylwSfShtLI2cqgVxdD8TDz6o4lxC0PsY03R/BYRERERERERERFpgRavoh5++OEoKmpYiLWKi4vNZbLncrb32lpaZRb1l24uQVFFdZMVImwNZtn/jNQ+im2tnIFLlas1V/i2sKVY4L+dN2UH1DfVUszZKqxHelLEaovANjYxwyUKG2o4Zblae0WSnuyscAm2FHOWpETg3Ka04Pc7tyMSWw1UHpy7w5DJGQixvRi/1x0+xStwcd5Po7ZtTYhn5Ymt5tH8FhEREREREREREYlrhcsHH3yA6urAArtTZWUlPv7445benHTSwGVLSTXW7diCv3++OvS1xoGLB+zs5QxcQi3FIoQJzvklkWahOPFmbMWKc/HdhgeRApeGmS/esCHd0bbfOYcm1sDFvT3mdmL43tTEwI8iK09qQhUuTX8fK0IqUBc2A6Y5DFzq4G+ocEnwhgUudu6Lu4XY7mgp1lzAtLuEAhdVuIiIiIiIiIiIiEg8Ape5c+eG/nvBggXYtGlT6PO6ujrTWqxPnz4tuW/pZGpdFS7z1u8Mu9xdIWIDg7CWYnY+SYQFfM5wsaFIc4GL83Z9EQKX4kgVLsHbdi7s52Y0EbjsQoWLm3ufNFfhwn1g91FTLcXc25Qe4/2YDKmuYYYLK0yyHIFLZnAWjDNg4e6Ntd1XSznbdu3qPm5rqnARERERERERERGRuAYue+21V7Bdkidi67DU1FQ88sgju7QR0jk4ik9M4OKeTZIcocKFbIsscxt2hkuEagZ3S7Fq5x1GYC93zlqx2xR5hkvjmS89MhpaiqW4FtidFS67GjhkpjQ/v4XSgoFJcUUgCKFEx/1HkuiYKxNrsBNqKdZMhQvDMjsjhu3E4tXCKzVp16qI4slWtmiGi4iIiIiIiIiIiMQlcFm5cqWZ2TF48GB8+eWXyMvLC12WlJSE/Px8+HxaoOxKM1x6ZqeGXR6tJZdtkeW8jUjtsmwIYFXVxFbh4nUEKFmOlmJfr96OTTurMG1cTxMYRKpw6ZHeUOGS0qhCx9PqGSaxzG9xtgRztkJrrsIlyfHz1pKWYmRbirHCxc5R4T5yPocMHmrrauNW3ULO+4vXnJiWstvhnmMjIiIiIiIiIiIi0iaBy4ABA8y/9Y5qBelaHLkJdpTXYED39LDLo7XkithSLMIiPttpOasKIlW4MBRg8Ed2NkyklmK87LH3l5v/Ht83G/26p0WscMnNbKhwSXNX6ITNcNm1Co/MmAOXwH2XcOhNjDNNnBUu9vub475J01IsJcG0DcvLTAqrZGHFT1lVfGeZOEONjjLDJSct8BrqFvxXREREREREREREpE0DF6fly5fj4YcfxsKFC83no0ePxuWXX44hQ4bsys1JJ8CgxBl/cL5KjSt8c7aHcoYqzuDEhh7OAIMBCL9eXlUbtugeaYYLL3dWzJBznZ7hACtVKh3VMsF8JtTazBm4sDLkpuNHBW7bFQK1xQyXWFuK2bDKGQo118bLVp6wwifWofbOaiDi9/XISMaVRw1HTlqS6zJf3CtPnK3QOkqFy5QhuUhPTsDoXlntvSkiIiIiIiIiIiLSibR4hfOtt94yAQvbio0fP958fPHFFxgzZgxmzJgRn62Udlfj7CcWrCCpcQUiKdFmuDgCl3o7w8URYNiKgtKqurAZLlW14S3GGoKIpkME92wZe582zHAHK0PyMsxHpPuyYg00drXCxd3OLJaAxwYurG6JdcaKbSnmfs7G9M5Gn5zwFnF2ps2uPvZYOCuaOsoMFwY/+w7sbkIXERERERERERERkVi1eEXxhhtuwJVXXol77rmn0devv/56HHXUUS29SekEbPsu5+fOcKSpGS7O69n/dC6ud0tLwrbSapRV1aK2riEsqXIEOraVGPMChgZ1tmzF1VLMBi6bd1aGPrfXDVWPxBhOOLcx3hUu7gHtzc1vMdsUDEJind9C7q5dTYUpNoxJDgYv8a5wieUxi4iIiIiIiIiIiHRULV5JZRuxCy64oNHXzz//fCxYsKCttks6GOccFtvuyxmIRKpwse3BaiJWuDQedM/rOduI2f8OhCyBrzFscVaemK+5Ps9xVbjYoMU+hlhnhfB++vdIQ25GcsyVKtb4vjnm+/cf3D2m63N/OB+XDaua/p6GCpdYufddU4GLvSzJt3tmuNiKHREREREREREREZEuUeGSl5eH2bNnY9iwYWFf59fy8/PbctukA7HzTyzmJlU1dU1WuNgKDGdYYwMXZ+jhDDPKHbNXGgIdT6CqBX4TvLgDFl8zLcVs4GIrXVpSSfF/x4822+xuQ9acy44YambNxDqXhBU8DB9Y5eMOpKJJCl6nJYGLs/UY90NTj2u3VLgkdryWYiIiIiIiIiIiIiJxDVx+9atf4ZprrsGFF16Iiy66CCtWrMCUKVPMZZ9++inuvfdeXHXVVbu0EdLx2UH1rEKwOUiZIxxxt4dyhirOsMY5FD70fYmcQRIIccodIY6tcAmELPzmQIWLu0DF/Xm0wMW2NnMHNE3hdX1oeasrBhtJCS37vlRH4BJTS7FQhUvsuamznVqyq41ZtAqXeM5wsa8Z7q+WPC8iIiIiIiIiIiIiHU3MK7V33HEHLr74Ytxyyy3IzMzEgw8+iBtvvNFc1rt3b9x+++247LLL4rmt0o5sWzCGAqXVdYFwpDoQDkRrCWUDgeoIFS7OKhVWgfCjqqYelY4Qpzp4nwxZ+L/QwrzH3+Qg+Oy0aC3FArfXURf2w+aZxNBSzFaepCfvWkuxlGYqV0IVLs0EM21R4dLScEpERERERERERESk0wYuHFhuF7yvvPJK81FSUmK+xgBG9myh+Sc+LxK8fhPAlFUFwpG+3VIxtCAT3dKTIgYuNuggW+ziDEl4PYY1DFycnDNc7NWZF/ibaSk2rk82xvXNxrx1OwP3b1uKBf+NJcxoD855JrG0FNtvUA+s3FqOA4fmxnwfA3ukYe328piClAMG98DaHRWYPKQH4qUgKxlThuaaf0VERERERERERES6zAwX5/wHUtDSddi2YAwCWI3CwMXOWzlzv/4Y1Sur0feEWoo5KlzsHBVnayuGLZFmnVTV1jkCF08oqAmvbwm/LcpMScQVRw7HvW8uwpJNJaGqmkjtzDoS5zyTWGbG9MlJxVVHDW/RfRw0LA8fL90a03X7dU9r8e23FJ/XCw4aFNf7EBEREREREREREelwgcvw4cMbhS5u27dvb+02SQee4cIgwOYCtuop2rBzO4ekJhjWfLx0C+auK2rUUiwx2FLMraHCxTYUC85yqQ9/DUZ7Sdogxj3DxQZBHY2zxVe8tnFIXnrovzcUVcTlPkRERERERERERES6ohYFLpzjkp2dHb+tkQ4/wyXJ50Gta96Ge3ZL45ZifmzaWYlnPl0VusyZJ7AlWaTQxgYurGqx12f44vU21LgwuIkWAtpKFhu4bC+rjvsQ+N05w2VXcF9NHZ6Hj5Zswdg++lkWERERERERERERaZfA5cwzz0R+fn6b3bl0Hg3VIZy3ErmSxc3OIWFYU1ETaA9mMUT5vxNGo7C4EkPyMqK0FAtWuDhmvpgZLqF6l8btxKIFLqzmWLW1zAQOYzpo0OCc4RJtn7aFHx8wAEPzMyK2gRMRERERERERERGROAcuzbUSkz2bncPCICDRHx6ORG0pFqzSYDsyW2XirEwZlJtuPii5yQqXYOoSDFj8jpdiU4UgzsDlk+Dckgl9s5GdmoiOPsPFhlXxwP1y4NDcuN2+iIiIiIiIiIiISFcUc+Bi53VI12TnsDBcqXeNrY8WDtgqjdq6elTVhle4uCtTIlW4WKaNWPD6znkuZL/eVODCCpvPlgcCl4OGddygwRm4xKulmIiIiIiIiIiIiIi0c+BSH1xwl66JVSp2mHt9WOQRvcIlNMOl3h+qVrHcM+GbDlwars+AxRnw2FAlEjt4nrNbSiprTVXNuA7aTsw9wyWeFS4iIiIiIiIiIiIi0s4zXKTrYpVKKAhwZQHRW4o1VJjYeSwWw49YbsNc11S1NMxwyXK0BGtqhoutfqkMzo/htic0cT/tLSWxYds68naKiIiIiIiIiIiISGOdZlX3pJNOQv/+/ZGSkoJevXrh7LPPxoYNG8KuM3fuXBx88MHmOv369cN9993Xbtu7585w8TYKR6JVYyQmNFS4uAMXd1CSnNBQ3eHGa9oOWx5X4OIObiK1NLP33dHbdKWEtRRThYuIiIiIiIiIiIhIZ9KxV6AdDjvsMLzwwgtYvHgx/vOf/2D58uU4/fTTQ5cXFxfj6KOPxoABA/D111/j/vvvx+23344//vGP7brdewpWqdhwxdn+i6EG56pEkhgMOEyFS7DKxHJnH821FLNlNaxayXEGLk3kEna7bDszG8B0hhkuTVX8iIiIiIiIiIiIiEjH02lail155ZWh/2aocsMNN+CUU05BTU0NEhMT8dxzz6G6uhpPPfUUkpKSMGbMGMyePRsPPfQQLrroonbd9j1BTb2d4eKF19FTrKlgwAYcDFyqg4GNZVuERQpcmJP4A3cXCllCM1y8HmQ7W4rFMMPF3ndHrxpxznDp6OGQiIiIiIiIiIiIiHTSwMVp+/btJmCZMmWKCVvo888/x9SpU03YYh1zzDG49957sWPHDnTr1i3ibVVVVZkPZ6UMMcjhhwRUVdeaf71geOFFvT8QYvg8vuj7qb7OXK+mtg7llTWh76EdZZVh3+f114cuT0tIQHlN4P7I7/ebD3N5fT3SEz0Nt+X3R71/f33gNsurAvfNCKMjP6cJCD5Ghk7cbx14W0Xiwb7m9doXkXjRcUZE4k3HGRGJNx1nRCTedJyJLNb90akCl+uvvx6PPvooysvLccABB+D1118PXbZp0yYMGjQo7PoFBQWhy6IFLnfffTfuuOOORl9/++23kZaW1uaPobOav4lxhQcLv5sPFrsUbgtUYJQnANOnr434PVsqgMJCL8q2MyVbg8IdDVUbn8zaDN+6b0Off7fDg8LCwOXpCUBZQ96CqiKAtR/bq4HE0kL0KF5qbpdqi3j/qyLe/3dbPGY7a3cC26uAmiau2xHU1Af2F83+ZjNqVznKfES6kBkzZrT3JojIHk7HGRGJNx1nRCTedJwRkXjTcSYcM4kOH7iwLRgrUJqycOFCjBw50vz3tddeiwsuuACrV682Ick555xjQpdoM0RiceONN+Kqq64Kq3Dp16+fmQeTlZW1y7e7p9nw8QosnLUEe+81AR6vF+tmbzBfL8hKwbRpoyN+z/qiCnz6+kJkJCdgeL8cFC7bGrrssIl9cOyYQCBG2cu3Yennq81/52emYHtZNWrrA9UevbJT4PN4kFBUgeG9snDS5P749L/zzWV9c1IxbdqoiPdfM2cjNszbiLyMZCSUVqFftzRMmxZ4LXVErOKZXjQbfvhxwP79MXVYbntvkshuP1OAv8yPOuqoUPWiiEhb0nFGROJNxxkRiTcdZ0Qk3nScicx2xurQgcvVV1+N8847r8nrDB48OPTfubm55mP48OEYNWqUCUZmzpyJyZMno2fPnti8eXPY99rPeVk0ycnJ5sONLya9oBoEGnIByUkJ8Hp98HoClRgpiQlR91Nacp25Xr3fA86t538fNCzXhDRHjipAomNuS1pyUug2kxJ8SE70ob46cJnP6zOzWnh5QoIP3TNSQ9etrPVHvf/kxARzPVaO8F/eZkd/TtOSE1BRXYeUJL3+pOvS8VdE4k3HGRGJNx1nRCTedJwRkXjTcSZcrPuiXQOXvLw887Er6oPVD3b+CkOXm2++2SRw9sEziRsxYkTUdmISu5q6QHurRJ8XXl9DUJLYxHD3hOD1aurqUc3EBcCg3HQcOiK/0XWTHOELwxXeTwXqzOecde8NVjHxX3u7VFrl6D3mwtuhqtrA7fi8Dd/XUaUk+kzgkhDcdhERERERERERERHpHDr+CjSAL774wsxumT17tmkn9t577+GHP/whhgwZYoIW+tGPfoSkpCTTcuy7777Dv/71L/zud78Laxcmu64uGHAxCEgKC1yiv4QSgmFMXb0flcHQIzmB01gacwY3/G/n52wZZ7vGuXMIG+Q0FbjY63SGECMlMbA/naGSiIiIiIiIiIiIiHR8nWJVl8Pr//vf/+KII44wFSsMVcaPH48PP/ww1A4sOzvbDLpfuXIlJk2aZNqV3Xrrrbjooovae/P3uAoXZzVKU4FLoqOipKwqGLgEAwW35LAKl/D7YNhis5KWzOvh3Bfy+8MDoI6sV3aq+Tc/s3GbOxERERERERERERHpuNq1pVisxo0bZ6pamsMQ5uOPP94t29TVsC2YDS0SY24p1nBZWbD1lzNYcUryNVS+sBIlI5lt4SpDbcScLcVs9QorZ5piK1yct9vR/fTgQdixd1/0zE5p700RERERERERERERkT2twkU6UIWL1+tq/9VESzFHwFFW3Uzg4prhkpHcEMCYW3G1FMtIaT4rdAcunWGGC1uuKWwRERERERERERER6Xw6/gq0dAi1doaLzxMWmjQ1a4Ttv2yVS1VNfZMzXJyBS6DCpSFQ8XobKlxsiNIjPanFgUtT1TgiIiIiIiIiIiIiIq2hwEViUuuY4eKsanEGJZGkJYVXokS7fljg4vMiI4UtxQIYk7ijkgsOGoz8rGScd+DAFlS4KHARERERERERERERkS48w0XaX01wXkqjGS7NhBjZqYkorqgJfR59hkv0ChdPhBkubLt192nj97gZLiIiIiIiIiIi0jnV19ejurq6vTdDpFVqamqQkJCAyspK1NXVoatITEyEzzFnfFcpcJGY1NTWh4KRsMCliZZiNnBZG0OFi7PdF0OdTMeMFmYsHtcMl1h0xhkuIiIiIiIiIiLS+TBoWblypQldRDozv9+Pnj17Yu3ateZE+K4kJyfHPPbWPG4FLhKTWlvh4vWEVaMkNtNSLCu1oTVYUzNc+CJmGFNdW2/uI905w8UELsEKlxYkLj7XD4YqXEREREREREREJB4L1Bs3bjRnx/fr1w9enfQrnRhDw9LSUmRkZHSZ17Lf70d5eTkKCwvN57169drl21LgIjGpratvaCmW4Im5pViOI3BhxUlTc1RYLcPAhZUozpZibCNmv6sl6WKjlmKOKhoREREREREREZG2UFtbaxZre/fujbS0tPbeHJE2aY2XkpLSZQIXSk1NNf8ydMnPz9/l9mJdZ4/JLquv96PO7w+FIkktbClmJSc2/SK17cYazXBxzG5xV620LHDRy11ERERERERERNqWnXORlJTU3psiIq1gA1POsdlVWoGWZtU4ek+yooVBhs09mqsayU5zBC7NtB8LBS4+DzIcM1wY9tjspCVdwdwtxNRSTERERERERERE4qWrzbsQ2dN42uBnWIGLNKumLlDdYqtE7LwVsv/GUuHS3HVt5QwDnfSkhmqY8uq6UMBjK11i4Z730lQ7MxERERERERERERGR1lDgIs1iEHLRwYNwWC9/KLSwrcRa0lLM2YosElsBk+ANhDpWeVVd6POWhIzu9mOqcBEREREREREREdkzHHroobjiiiuwJ3nmmWeQk5MT1/sYOHAgHn744bjeR1emwEWaxcqUfQZ0w7DshkqXXQlcYrmfSJUoZdW1ocqWllS4uG9HFS4iIiIiIiIiIiIB5513njnJmR+JiYkYNGgQrrvuOlRWVrb3pnVKDDLs/nR+3HPPPTHfxg9+8AMsWbIkrtsp8dUwKEOkBRoCl6ZDjJTEhtZgVbWBAWLR2AoYdyVKhbOlWAsiQnfA0lw4JCIiIiIiIiIi0pUce+yxePrpp82Q8K+//hrnnnuuCQnuvfdedAR+vx91dXVISOg4y9jcVwyoIvnVr36FCy+8MOxrmZmZMd92amqq+ZDOSyvQsksm9M1Gdloi+nVPi/l7GJw0ZWyfbKQlJ2Bofkajy2x00pIKF3dwowoXERERERERERHZHSFBZU1du3zwvlsiOTkZPXv2RL9+/XDKKafgyCOPxIwZM0KX19fX4+677zbVLwwCJkyYgH//+9+hy/fZZx888MADoc95GwwjSktLzefr1q0zAc6yZcvM53//+9/N9zCE4P3+6Ec/QmFhYej7P/jgA3P9N954A5MmTTLb98knn6CsrAznnHMOMjIy0KtXLzz44IPNPrbbb78de+21F5588knz+NLS0vD9738fO3fuDLven//8Z4waNQopKSkYOXIkHnvssdBlq1atMtvzr3/9C4cccoi5znPPPRf1Pu3jcn6kp6eHPbb//e9/GD9+vLmtAw44APPnz4/aUmzOnDk47LDDzO1mZWWZffLVV1+FLv/Pf/6DMWPGmP3EChv3fuG+PfHEE81zx+cw0rYXFRXhpz/9KfLy8sx98DUwb968ZvevRNZxokHpVM7crz9+sG+/sFkrzamsqW/y8sNG5uPQEXkRb9MGLS25P68rYNEMFxERERERERERibeq2npc+tw37XLffzhr77COMy3Bhf/PPvsMAwYMCH2NYcuzzz6LJ554AsOGDcNHH32EH//4x2ZxngEEPxgkXHPNNSbs+fjjj01gwJCE1TMffvgh+vTpg6FDh4aqQ+68806MGDHChAFXXXWVaW02ffr0sG254YYbTJAzePBgdOvWDddee625rVdeeQX5+fm46aab8M0335hApSkMel544QW89tprKC4uxgUXXICf//znoeCB/95666149NFHMXHiRHz77bemQoUhCat9nNvDMIPXYVDSGnwsv/vd70wYw8fBQIRtxCJVzZx11lnmPh9//HH4fD7Mnj07dD1WJDFAYrDEVmR87vjYevToYfYp8d8NGzbg/fffN9932WWXhQVcdMYZZ5hAhiFXdna2ea4ZnC1evBi5ubmteqxdkQIX2WUtCT+opq6+RbeZ4POgti6QyodairXgLlXhIiIiIiIiIiIiEt3rr79uqkZqa2tRVVUFr9drwgfi57/5zW/wzjvvYPLkyeZrDEAYprBqhGELB9f/5S9/MW2/GNgkJSWZxX+GMAxc+C+vZ51//vmh/+Zt/f73v8e+++5rKmK4Hc7WXEcddZT5b17G+2Dwc8QRR5iv/fWvf0Xfvn2bfXycR/O3v/3NhD70yCOP4PjjjzfhCQOP2267zfz3aaedZi5nFciCBQvM43MGLldccUXoOk25/vrr8X//939hX2OQcfDBB4c+533ax2Yfx0svvWTCE7c1a9aYgIaVN8TQy3rooYfM/rjlllvM58OHDzfbfv/995ughSEO7/vLL780+5i4H1nNY/G55OUMYVglQ/x+bg8rmS6++OJmH7OEU+AiccfWYzvLa9Azu2Xp75C8DCzeVBI2fyWhBUNc3BUumuEiIiIiIiIiIiLxlpzgNZUm7XXfLcF2VayeYMuu3/72t2ZWyve+971QdUh5eXkoHLCqq6tN1QUxSCgpKTGVIaywsCGMHRTPqhQGBharMliRwVZZO3bsMC3LbLAwevTo0PXYdsxavny5uc/9998/9LXu3bubKpnm9O/fPxS2EIMj3ierN9imi7fNqhfn3BWGT6z0cHJuT1P4WG11ieW8f7sN7sexcOHCiLfHCiC2+2IrNrb6YjXKkCFDzGX8npNPPjns+gceeCAefvhhE4Dxcj6fbENmMbhxtyxjoMWqGKeKigqsWLEipscs4RS4SNxde8wI/G/uRpwwvneLvu+iqYPx76/X4fCR+SZoYYXMfoO6x/z9qnAREREREREREZHdjR1cdrWt1+7G1lm23ddTTz1lZrSwCoIhhJ3Dwpkj7tDAVkNw8Z7fw0qWzz//3IQzU6dONVUurLBYunRpqMKFoc4xxxxjPtjKi23JGLTwcwYq7u2KN/v4/vSnP4WFOcT2XbuyPWzBZfdnW2A4xTk3fA5YrcLqmOeffx6nnnpqm+0DzsTh82cxkOLXY6kgksYUuEjc9cpOxU8PHtzi78tJSwr7vpbehp37YiX6FLiIiIiIiIiIiIhEwnZinCnCqgou8rPihMEKQxFnWzA3XsYZIWxNddddd5mqDbat4n9zMZ+trmjRokXYtm2bqX7hEHtyDoCPhhUdnD/yxRdfmIoVYnUMA52mtou47Zxh0rt34ETwmTNnmsfJqpKCggLzdVZycFbK7sJtcD8OZ5svN+4/flx55ZX44Q9/iKefftoELvyeTz/9NOy6/JzXZWDEahZW67CqyLYUY2VPUVFR6Pp77703Nm3aZCphBg4cGApcOO8mKysrTntgz6YeS7LHcle0+FrQjkxERERERERERKSrYcsqLtb/4Q9/MC23rrnmGrPQz1kjbL/FQfWcg8LPLbYQe+utt8yivZ01wq+xisUZiDBk4IwXfj9DjldffRV33nlns9vE2S6suGG7rvfee8/MimHbLgYnzeGAe85iYeusjz/+2AyN56wUzm+hO+64A3fffbeZJcPgY968eSbQ4HyUXcH2agwwnB8ML5w4n+bdd98NPQ5WxXBIvRvbev3iF78w1SerV682YcqsWbNC4czVV19tbof7kNvO54Tzd/icEUMlztH52c9+ZsIqBi9sT5aamhq6D7YpY4sz3v/bb7+NVatWmdZwvM1YwjBpTCvQssdytxRzfy4iIiIiIiIiIiINGJpwkf++++4zLcC48M6h7AwluNDPBXy2t+JweYtzXFgV4QxXGLhwjgj/tdhC7JlnnsGLL75oqmdY6fLAAw/EtF0c5M77OfHEE01IcNBBB4XNJomG7b047H7atGk4+uijMX78eDz22GOhyxlA/PnPfzYhy7hx48xj4DY6H19L3Hrrraaqx/lx3XXXhV2Hj/vyyy83289A5rXXXjNBlBuDL1YEnXPOOaZqhUHRcccdZ0IiW53ywgsvmBZjY8eONffNMMc5Q4aPi1U8fFzcDxdddBHy8/PD2t9Nnz7dtIH7yU9+Yu6H1U1r1641FUDSch6/3+/fhe/bYzFx5FCknTt3qmzKoaamxvzw8eDEEr7O4qd/nQX7Cr/zlLHondOQ4IpIx9JZjzMi0nnoOCMi8abjjIjEm44zHVNlZSVWrlxpFulZUSEdA+efvPzyy5g9ezY6AlaqHHbYYaaNmHNwfUfjbCkWSxVRV/lZLo4xN+hae0y6dFsxVbiIiIiIiIiIiIiISLwocJGuE7j49HIXERERERERERERkfjQCrTs0XyOsjdn+CIiIiIiIiIiIiJ7dkuxjtJOjDjPhtM9OnI7MWk9BS6yR/M5MpZE5yciIiIiIiIiIiIiIm1IgYvs0VThIiIiIiIiIiIiIiK7gwIX2aM5x7YkOMIXEREREREREREREZG2pBVo2aPZqhaPB1CBi4iIiIiIiIiIiIjEiwIX6RKBC//1MHUREREREREREREREYmDThe4VFVVYa+99jKL57Nnzw67bO7cuTj44IORkpKCfv364b777mu37ZSOwRcMWdROTERERERERERERETiqdOtQl933XXo3bt3o68XFxfj6KOPxoABA/D111/j/vvvx+23344//vGP7bKd0jH4gkGLrXQRERERERERERGRjo8n3L/88stxu32uHfPE/s4m3vtFulDg8sYbb+Dtt9/GAw880Oiy5557DtXV1XjqqacwZswYnHnmmbjsssvw0EMPtcu2SsfgC77CE3wKXERERERERERERJyBAxfvnR8jR44Mu05lZSUuvfRS9OjRAxkZGfje976HzZs375bt27hxI4477jh0ZOedd57ZbxdffHGjy7jfeBmv05n3y89+9jP4fD68+OKLu+0+O7MEdBL8Qb7wwgtNepeWltbo8s8//xxTp05FUlJS6GvHHHMM7r33XuzYsQPdunWL2qKMH85KGaqpqTEfEmD3RWfbJx74Ue+vN8liZ9t2ka6msx5nRKTz0HFGROJNxxkRiTcdZzomPh9+vx/19fXmo7PgNvPEdZ7gbiUkJIQ9hiuuuALTp0/Hv/71L2RnZ5sT3E877TR8/PHHcd++/Px882+89ikff2tvn7fB0RbPP/88HnzwQaSmpoaCqn/84x/o379/6LXR2fYL/y0tLTWP7dprr8Vf/vIXE7jFU3V1ddj6/u7GfcrHzZ9phkxOsR53O0XgwgfJJJBJ4T777INVq1Y1us6mTZswaNCgsK8VFBSELosWuNx999244447Gn2dB5pIwU5XN2PGDHQmK9d4UVgOVCUB06evbu/NEZE98DgjIp2PjjMiEm86zohIvOk407EwpOjZs6dZnOaCcWfBk9BZgeFeA7UnpO/cudN0E/rTn/5k1mTpd7/7Hfbff3+8++672HfffaPeNk+O/9WvfmVmcHfv3h0nnHACbr31VqSnp5vLx48fj7PPPhuLFi3Cm2++acKcK6+80pxwb3E999lnn8Xxxx9v9uvNN9+M1157DUVFRcjLy8NPfvITXHXVVea6a9euxfXXX4+PPvoIXq8XRxxxhDkR34YT9Nvf/haPP/44KioqcMopp5iqnbq6utDjpb/97W/4wx/+gNWrV5uw5KKLLsJPf/rTqI+Ti/Djxo3DypUrTQem73//++brrAbp06ePGX/B69j74D7nfvjvf/+LkpIS09LsN7/5Dfbee2+z2M/b4mO64IILwuaWH3rooWZfcpuc+2XNmjWYMGGC2W6O1uCojcGDB5uuT/vtt1/oNv7617+aeecsTDj88MMxefJk8zkfZzTcvn/+858YMWIELrnkEowePRoLFixA3759zePh13m/Rx11VOh7Xn/9dXPdxYsXm9fVunXrcMstt+C9994zzwvv95577jGPg37+85+b19nEiRNNoMOwZc6cOSbkefLJJ7Fs2TJzO5zbznV8Pu8Wg0De9vr1681r8Uc/+pG5PWYHfD3F8jp04+uMrw++jmpra8MuKy8vR4cPXG644Qbzwm/KwoULTfjBJ/jGG29s823gbdofTOKLhakk58FkZWW1+f11Vjww8Jc5f4ASExPRWSx9dxlqNxajT04qpk0b1d6bIyJ74HFGRDoPHWdEJN50nBGReNNxpmNiNQMX/NlyKyUlxXzN89Z+QOWm3b8xKT3hP+bLmK6anJyMFStWmIV0bvcBBxxgFv/tYvhXX31lXnMnnnhiaJ2UwQsvnzdvngk1Ilm+fDnOOOMM3HnnnXjmmWewZcsWUxnDwIQBDnHx/ZFHHjFrs3fddZdZ/+UaLYMY5wI+K0Z436weeeutt0ylDe+f+5sfvIxBxTnnnGP2//vvv28Wyn/5y1+asIQL/fTCCy+YdWje50EHHWQCC/43wwn72BiYMAz4/e9/bwKAb7/91rTTYjBz7rnnRnys/Dlk4MZQhvdhwxluJ0OTDz74wFzH3gcrhhhKcL8wjOEc8tNPPx1LliwxgcAPf/hD0+GJ4ZP1yiuv4MADD8TYsWMb7Rc+ZuLzxgBl2LBh+L//+z/z2Hmb3LZPP/3U7Fs+Nj6XDMsYOjBsi7T+zeIHrsVnZmaawIX7luvlxx57rAmKePv8PgY+3DZn1ctLL72Ek08+2QSQfO0wgOLrigEGt4XPNb/GAIThCvcNL+Njt5VWvG1e99e//rUJdQoLC3HNNdeY19D//vc/cx0GXCzQ4Ne4n/lccfY7cbt5G7G8DiP9LHPfspOW/Vm2nMFchw1crr766mZ72PFFzx8MplE8CDjxB/yss84yCR2fRHf/QPs5L4uGt+m+XeKTrV9c6PT7JTEhAV6PF4kJvk613SJdWWc7zohI56PjjIjEm44zIhJvOs50LKyS4OI1QwR+GAxbKta3y/Z47DY0gwvhXIjmojbngrAL0CGHHIL58+ebRWsudHNRnIvh7q5CXHcNPVYXBhtcs7WhAW+fIQZv+4knnggtZDNEsCfYc3bMZ599ZipoOCbCsvuU4QrDBC6Ec187Ox0xQGAAxEV4BgPEygu2S2PFB6sfeP9cmLcVNFz45/dxgd0+Dj5+BjsMQGjIkCGmAocVPqymibivg7NvWK1z0003me0khhys0vjwww9Dr42ysjLz+LnPGVbQn//8ZwwcOBBPP/20adv14x//2FSnsDKEwRLDJIY3DDmc+9vuF/s1BhIMU4gVHXzsDNO4X1mxw5kvvH27r7nWzuAn0nNoW5WxumTmzJkmZOH1+BgZ3LCqhI+J28qvcR+yCoWBxPTp003owuuzyoe3xcoVXp/42HNyckzIwoIHfp3VJra6xXJWFQ0dOtQ8f3weWWXCkInPCV9Xdtb7qFGjTPUNn1e7X2J9HTrx+7hNkY6xsR5z2zVwYQmQswwoGu4IJlrWhg0bzA8eX2wsYSOWIzGdYnJmHzwTf+7IaO3EZM/nCx4zEryBH2oREREREREREZG4S+3Z4e/XOXidlSVcZ2XVBSs1nC2tmsKFfduWim2f3njjDdMSim2wWDFi2TkmDEW4OG7Xc534+cMPPxzxfnjSPitfuNbLSgu2huKCve2QxKDFhi3Eqh0u7PMyLtTzX/dge94fK2KIYQgrIpyhDLFaxranagrXuBmiMFDgY+V/5+bmhl2Ht8+1awZNFtex2fqL20dsMcb9w/kv7A7FwIbBFys1msLnz+rVq5f5l9/HcIXtvU499dSw6/M+Gbg0hSEQ1+Dt45g2bZrZPyyOYHUTP+f2v/rqqzjzzDPxn//8x1SWHHnkkeb6fB0wtGF458SAhvvCYhs199wWBmW33367uQ22QbMhEFuo8bnlY3K3tHO2ULP3H8vrsK11ihkutozNsqVSTBnZM47Yo40pJJ909utjEstElL35pOvyBVPaBJu8iIiIiIiIiIiIxNuxX6GzYUAxfPhws0huuwZxpgVnpvAyi9UttqMQKxrsMHE7MJ6zbNiKi+2bmlvnjRVnnHCRnIHOO++8Y9pScWH/3//+N9oCt5lYOWFP8Lfcw9OjOf/88/GLX/zC/DerSnYVqzJs4MJ/GTCxrVlTnNUXtprEhhS7WrXFKiHORmd7L+fX2Y6LgQtDElYDcRsZuPDfH/zgB6Hrc59OmjQpLPCwnEUY7nkqDL8Y9PCD38vrMmjh5y2ZkRSP1+EeE7jEgkkj+7xdeuml5olk8sZedOxXJ12XKlxERERERERERERiW6Bm5QHbRBHXWLmQz9Zbdk4HKwu4+G2rU1gREykcYXsntoJqCttVuT9vquqA1RNc0OcHF/oZRGzfvt18j53pYqtceP8MilgNQbzOF198YeaRRLp/tknr3bu3acPFwGNXcHsYCDDwcLZFs1g8wJCC7cbsfmNYNWvWLDPbxWJhAVuIscqDgRLbX7UGq4J4H07uz93sTHXORnEGTixyYHs1G8JxX7Hy6LvvvjOVL792dKni64AdqvLz81s0K51t3LZt22Zmztjnk/OE3I+JYV9TjynW12Fb65SBC/vasfwnUunUxx9/3C7bJB27wsWnwEVERERERERERCTEzv3g4j9HONx2221mcZ2D2+0J7uwmxLkdnOPCRXMOo2fYwvkv0bD7EC9ntQdncbCCgQvfHP/w6KOPhq7H4IGD3k855RRzGWd+2KHobpxrwlZZHGZv54OwyoaL/qx0YVsqLv6zJRnbgP385z83szo4A5wuv/xy05aMn7OlFysnGBJwfrjF7kmshuDjZnhSVVVlFvrZ0or7oDncd7Y1WKSqGO6HSy65xMxS4f5klQUfP+eSOFu4ce17ypQp5musKDnppJPQGnzOOPuG+5DPN4MRVgrZSphInn32WdMybMKECWFfZ4DFmSjcfyx84O3yeeC+51yd/R3VQfza/fffj5NPPtnMlWGnKraf40wYDri3navcuF8YTD3yyCOmDRxDHg6+d2LlCh8PX2vcT7Nnzzbt3Mg+rlhfh21NfZZkj+YLHjdU4SIiIiIiIiIiItKAg9kZrrBagC262LaKVR/Odk8c18B5KaxwsYvrXDBvCk+K5+yRJUuWmLkuDEnYiYgVJE5XX321CTR4OSsjuIAeqTKEOAeE4QQDE87uWLVqlalwsEPOX3nlFTPHm9vIAIZBCqsrLFbFcNg7F/pZucOFf4YfTlyU5xB7zi5hgMPAhov4DBJixVCqqWoOVm1wX7KKiBUYbN/21ltvNZpBzrCCM0g4e8W2attVDJhYJcP9ywDlzTffNKFJpKHxtmUcK1xOO+20Rpdxf3ObOOSeuO/5GuK2uiuD0tLS8NFHH5kAhbfFKiOGI5zh0tQ+4uuP+52hGgMe7rMHHngg7Dp8Tlj9w9ciX2+PP/64me9OycnJLXodtjWPP1KpSBdWXFxsUsydO3e2qNRpT8fyNh7E7DCkzuLvM1fjg0WF2HdQd1x8yJD23hwR2QOPMyLSeeg4IyLxpuOMiMSbjjMdExeQOV+Ei8DRFrElHKs42EbL2UpLdp8LL7zQtO6K1C2Ks1+4Rs61cQYsncVdd91lgiW2lovHz3KsuUGnbCkmEitfsIRMFS4iIiIiIiIiIiLSFbFChLNW2FaL7cT++te/4rHHHkNn9thjj5lqJ1ZmsT0d25exfVh7U+AiezQbtGiGi4iIiIiIiIiIiHRFX375pWnJVlJSYtqt/f73vzct1DqzpUuXmlZ027dvN23L2KLuxhtvbO/NUuAiezZvMGhJ8HWe8jcREREREREREZE9GWewyO7zwgsvYE/z29/+1nx0NFqFlj1afmZgSFJeRuBfEREREREREREREZF4UIWL7NEOHpaLofkZ6JWtgWUiIiIiIiIiIhI/fr+/vTdBRNr5Z1gVLrJH83g86J2Tav4VERERERERERFpaz6fz/xbXV3d3psiIq1QXl5u/k1MTNzl21CFi4iIiIiIiIiIiMguSkhIQFpaGrZs2WIWar1eneMunVd9fb0JDysrK7vMa9nv95uwpbCwEDk5OaEQdVcocBERERERERERERHZReys0qtXL6xcuRKrV69u780RaXX4UFFRgdTUrtc1KCcnBz179mzVbShwEREREREREREREWmFpKQkDBs2TG3FpNOrqanBRx99hKlTp7aqtVZnw8famsoWS4GLiIiIiIiIiIiISCux/VJKSkp7b4ZIqzB0qK2tNa/lrhS4tJWu0YRNREREREREREREREQkjhS4iIiIiIiIiIiIiIiItJICFxERERERERERERERkVbSDBcXv99v/i0uLm7vTelww5LKy8vNflHvPhGJBx1nRCTedJwRkXjTcUZE4k3HGRGJNx1nIrN5gc0PolHg4lJSUmL+7devX3tvioiIiIiIiIiIiIiIdKD8IDs7O+rlHn9zkUwXU19fjw0bNiAzMxMej6e9N6dDJXgModauXYusrKz23hwR2QPpOCMi8abjjIjEm44zIhJvOs6ISLzpOBMZYxSGLb1794bXG31SiypcXLiz+vbt296b0WHxh0w/aCISTzrOiEi86TgjIvGm44yIxJuOMyISbzrONNZUZYsVPYoRERERERERERERERGRmChwERERERERERERERERaSUFLhKT5ORk3HbbbeZfEZF40HFGROJNxxkRiTcdZ0Qk3nScEZF403GmdTx+TnsRERERERERERERERGRXaYKFxERERERERERERERkVZS4CIiIiIiIiIiIiIiItJKClxERERERERERERERERaSYGLiIiIiIiIiIiIiIhIKylwkWb94Q9/wMCBA5GSkoL9998fX375ZXtvkoh0EnfffTf23XdfZGZmIj8/H6eccgoWL14cdp3Kykpceuml6NGjBzIyMvC9730PmzdvDrvOmjVrcPzxxyMtLc3czrXXXova2trd/GhEpDO455574PF4cMUVV4S+puOMiLTW+vXr8eMf/9gcR1JTUzFu3Dh89dVXocv9fj9uvfVW9OrVy1x+5JFHYunSpWG3sX37dpx11lnIyspCTk4OLrjgApSWlrbDoxGRjqaurg633HILBg0aZI4hQ4YMwZ133mmOLZaOMyLSEh999BFOPPFE9O7d2/x99PLLL4dd3lbHlLlz5+Lggw8268b9+vXDfffdh65OgYs06V//+heuuuoq3Hbbbfjmm28wYcIEHHPMMSgsLGzvTRORTuDDDz80i5wzZ87EjBkzUFNTg6OPPhplZWWh61x55ZV47bXX8OKLL5rrb9iwAaeddlrYHx9cBK2ursZnn32Gv/71r3jmmWfMGwMREadZs2bhySefxPjx48O+ruOMiLTGjh07cOCBByIxMRFvvPEGFixYgAcffBDdunULXYeLC7///e/xxBNP4IsvvkB6err5u4mBr8UFi++++868J3r99dfNQshFF13UTo9KRDqSe++9F48//jgeffRRLFy40HzO48ojjzwSuo6OMyLSElx34TouT6SPpC2OKcXFxWaNZ8CAAfj6669x//334/bbb8cf//hHdGl+kSbst99+/ksvvTT0eV1dnb93797+u+++u123S0Q6p8LCQp6i5f/www/N50VFRf7ExET/iy++GLrOwoULzXU+//xz8/n06dP9Xq/Xv2nTptB1Hn/8cX9WVpa/qqqqHR6FiHREJSUl/mHDhvlnzJjhP+SQQ/yXX365+bqOMyLSWtdff73/oIMOinp5fX29v2fPnv77778/9DUee5KTk/3//Oc/zecLFiwwx51Zs2aFrvPGG2/4PR6Pf/369XF+BCLS0R1//PH+888/P+xrp512mv+ss84y/63jjIi0Bo8NL730UujztjqmPPbYY/5u3bqF/c10/fXX+0eMGOHvylThIlHxLE+mkywps7xer/n8888/b9dtE5HOaefOnebf7t27m395jGHVi/M4M3LkSPTv3z90nOG/bNtRUFAQug7PuuCZFDzTQkSEWE3HKhXn8YR0nBGR1nr11Vexzz774IwzzjAtBydOnIg//elPoctXrlyJTZs2hR1nsrOzTTtm53GGrTh4Oxavz7+veFapiHRtU6ZMwbvvvoslS5aYz+fMmYNPPvkExx13nPlcxxkRaUttdUzhdaZOnYqkpKSwv6MWL15sKoS7qoT23gDpuLZu3WpabDgXH4ifL1q0qN22S0Q6p/r6ejNTgS05xo4da77GX/D8xcxf4u7jDC+z14l0HLKXiYg8//zzpvUpW4q56TgjIq21YsUK0+qHrZZvuukmc6y57LLLzLHl3HPPDR0nIh1HnMcZhjVOCQkJ5iQUHWdE5IYbbjAnevCkEJ/PZ9Zi7rrrLtPOh3ScEZG21FbHFP7L2VPu27CXOduvdiUKXEREZLedfT5//nxzppaISFtZu3YtLr/8ctNXmIMaRUTicdIIz+78zW9+Yz5nhQvf07DnOQMXEZHWeuGFF/Dcc8/hH//4B8aMGYPZs2ebk9U47FrHGRGRzkUtxSSq3Nxcc2bF5s2bw77Oz3v27Nlu2yUinc8vfvELM2Dt/fffR9++fUNf57GE7QuLioqiHmf4b6TjkL1MRLo2tgwrLCzE3nvvbc644seHH35oBkDyv3mGlY4zItIavXr1wujRo8O+NmrUKKxZsybsONHU3038l8cqp9raWmzfvl3HGRHBtddea6pczjzzTNPm9Oyzz8aVV16Ju+++21yu44yItKW2Oqbo76jIFLhIVCyRnzRpkukj6jy7i59Pnjy5XbdNRDoHzmZj2PLSSy/hvffea1RqymNMYmJi2HGGvT65gGGPM/x33rx5Yb/oeSZ7VlZWo8UPEel6jjjiCHOM4Jmg9oNnorMFh/1vHWdEpDXYDpXHDSfOWRgwYID5b76/4aKC8zjD1kDsb+48zjD4ZUhs8b0R/75iv3QR6drKy8vNXAQnngDLYwTpOCMibamtjim8zkcffWRmZjr/jhoxYkSXbSdm+EWa8Pzzz/uTk5P9zzzzjH/BggX+iy66yJ+Tk+PftGlTe2+aiHQCl1xyiT87O9v/wQcf+Ddu3Bj6KC8vD13n4osv9vfv39//3nvv+b/66iv/5MmTzYdVW1vrHzt2rP/oo4/2z5492//mm2/68/Ly/DfeeGM7PSoR6egOOeQQ/+WXXx76XMcZEWmNL7/80p+QkOC/6667/EuXLvU/99xz/rS0NP+zzz4bus4999xj/k565ZVX/HPnzvWffPLJ/kGDBvkrKipC1zn22GP9EydO9H/xxRf+Tz75xD9s2DD/D3/4w3Z6VCLSkZx77rn+Pn36+F9//XX/ypUr/f/973/9ubm5/uuuuy50HR1nRKQlSkpK/N9++635YATw0EMPmf9evXp1mx1TioqK/AUFBf6zzz7bP3/+fLOOnJaW5n/yySf9XZkCF2nWI488YhYpkpKS/Pvtt59/5syZ7b1JItJJ8Jd6pI+nn346dB3+Mv/5z3/u79atm/nFfOqpp5pQxmnVqlX+4447zp+ammr+8Lj66qv9NTU17fCIRKQzBi46zohIa7322msmmOXJaCNHjvT/8Y9/DLu8vr7ef8stt5hFB17niCOO8C9evDjsOtu2bTOLFBkZGf6srCz/T37yE7MYIiJSXFxs3rtw7SUlJcU/ePBg/8033+yvqqoKXUfHGRFpiffffz/iegwD3rY8psyZM8d/0EEHmdtgcHzPPff4uzoP/6+9q2xEREREREREREREREQ6M81wERERERERERERERERaSUFLiIiIiIiIiIiIiIiIq2kwEVERERERERERERERKSVFLiIiIiIiIiIiIiIiIi0kgIXERERERERERERERGRVlLgIiIiIiIiIiIiIiIi0koKXERERERERERERERERFpJgYuIiIiIiIiIiIiIiEgrKXAREREREZEuZ9WqVfB4PJg9e3bc7uO8887DKaecErfbFxERERGRjkWBi4iIiIiIdDoMMxiYuD+OPfbYmL6/X79+2LhxI8aOHRv3bRURERERka4hob03QEREREREZFcwXHn66afDvpacnBzT9/p8PvTs2TNOWyYiIiIiIl2RKlxERERERKRTYrjC0MT50a1bN3MZq10ef/xxHHfccUhNTcXgwYPx73//O2pLsR07duCss85CXl6euf6wYcPCwpx58+bh8MMPN5f16NEDF110EUpLS0OX19XV4aqrrkJOTo65/LrrroPf7w/b3vr6etx9990YNGiQuZ0JEyaEbZOIiIiIiHRuClxERERERGSPdMstt+B73/se5syZY8KUM888EwsXLox63QULFuCNN94w12FYk5ubay4rKyvDMcccY8KcWbNm4cUXX8Q777yDX/ziF6Hvf/DBB/HMM8/gqaeewieffILt27fjpZdeCrsPhi1/+9vf8MQTT+C7777DlVdeiR//+Mf48MMP47wnRERERERkd/D43addiYiIiIiIdIIZLs8++yxSUlLCvn7TTTeZD1avXHzxxSY4sQ444ADsvffeeOyxx0yFCytNvv32W+y111446aSTTMDCwMTtT3/6E66//nqsXbsW6enp5mvTp0/HiSeeiA0bNqCgoAC9e/c2Acq1115rLq+trTW3P2nSJLz88suoqqpC9+7dTVAzefLk0G3/9Kc/RXl5Of7xj3/EcW+JiIiIiMjuoBkuIiIiIiLSKR122GFhgQox1LCcwYb93LYQc7vkkktMNcw333yDo48+GqeccgqmTJliLmPFC9t/2bCFDjzwQNMibPHixSb02bhxI/bff//Q5QkJCdhnn31CbcWWLVtmgpWjjjoq7H6rq6sxceLEVu0HERERERHpGBS4iIiIiIhIp8QAZOjQoW1yW5z1snr1alO5MmPGDBxxxBG49NJL8cADD7TJ7dt5L//73//Qp0+fRrNoRERERESk89MMFxERERER2SPNnDmz0eejRo2Kev28vDyce+65plXZww8/jD/+8Y/m6/wezoHhLBfr008/hdfrxYgRI5CdnY1evXrhiy++CF3OlmJff/116PPRo0ebYGXNmjUmJHJ+9OvXr40fuYiIiIiItAdVuIiIiIiISKfEuSibNm0K+xpbedlh9xxuz7ZeBx10EJ577jl8+eWX+Mtf/hLxtm699VYzb2XMmDHmdl9//fVQOHPWWWfhtttuM2HM7bffji1btuCXv/wlzj77bDO/hS6//HLcc889GDZsGEaOHImHHnoIRUVFodvPzMzENddcY+a8sBUZt2nnzp0muMnKyjK3LSIiIiIinZsCFxERERER6ZTefPNNU1nixIqTRYsWmf++44478Pzzz+PnP/+5ud4///lPU2kSSVJSEm688UasWrUKqampOPjgg833UlpaGt566y0Tquy7777mc857YahiXX311WaOC4MTVr6cf/75OPXUU02oYt15552miubuu+/GihUrkJOTg7333hs33XRTnPaQiIiIiIjsTh6/neIoIiIiIiKyh/B4PHjppZdwyimntPemiIiIiIhIF6EZLiIiIiIiIiIiIiIiIq2kwEVERERERERERERERKSVNMNFRERERET2OOqcLCIiIiIiu5sqXERERERERERERERERFpJgYuIiIiIiIiIiIiIiEgrKXARERERERERERERERFpJQUuIiIiIiIiIiIiIiIiraTARUREREREREREREREpJUUuIiIiIiIiIiIiIiIiLSSAhcREREREREREREREZFWUuAiIiIiIiIiIiIiIiKC1vl/J7WjtDXCuVcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 2000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting episode rewards\n",
    "plt.figure(figsize=(20, 3))\n",
    "plt.plot(simple_episode_rewards, label='Reward per Episode', alpha=0.7)\n",
    "plt.title('Simple Agent: Total Reward per Episode')\n",
    "plt.xlabel('Episode')\n",
    "plt.ylabel('Total Reward')\n",
    "plt.grid(True)\n",
    "\n",
    "# Calculate and plot moving average\n",
    "window_size = 50\n",
    "if len(simple_episode_rewards) >= window_size:\n",
    "    # Use numpy's convolve for moving average\n",
    "    rewards_ma = np.convolve(simple_episode_rewards, np.ones(window_size)/window_size, mode='valid')\n",
    "    # Adjust x-axis for moving average plot\n",
    "    plt.plot(np.arange(len(rewards_ma)) + window_size - 1, rewards_ma, \n",
    "             label=f'{window_size}-episode Moving Average', color='orange', linewidth=2)\n",
    "    \n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation:** This plot shows the total reward collected by the agent in each episode. A noisy line is expected due to exploration and randomness. The orange line (moving average) smooths this out, showing the general trend. If the agent is learning effectively (even with its simple strategy), we expect the moving average to trend upwards, indicating it's getting higher scores over time (reaching the goal more often or faster, avoiding walls more).\n",
    "\n",
    "#### 7.1.2 Episode Lengths Over Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting episode lengths\n",
    "plt.figure(figsize=(20, 3))\n",
    "plt.plot(simple_episode_lengths, label='Steps per Episode', alpha=0.7)\n",
    "plt.title('Simple Agent: Steps per Episode')\n",
    "plt.xlabel('Episode')\n",
    "plt.ylabel('Number of Steps')\n",
    "plt.grid(True)\n",
    "\n",
    "# Calculate and plot moving average\n",
    "window_size = 50\n",
    "if len(simple_episode_lengths) >= window_size:\n",
    "    lengths_ma = np.convolve(simple_episode_lengths, np.ones(window_size)/window_size, mode='valid')\n",
    "    plt.plot(np.arange(len(lengths_ma)) + window_size - 1, lengths_ma, \n",
    "             label=f'{window_size}-episode Moving Average', color='orange', linewidth=2)\n",
    "    plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation:** This plot shows how many steps it took the agent to complete each episode. If the agent learns to reach the goal more efficiently, the number of steps should decrease over time. A downward trend in the moving average indicates improved efficiency.\n",
    "\n",
    "#### 7.1.3 Epsilon Decay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting epsilon decay\n",
    "plt.figure(figsize=(20, 3))\n",
    "plt.plot(simple_episode_epsilons)\n",
    "plt.title('Simple Agent: Epsilon Decay Over Episodes')\n",
    "plt.xlabel('Episode')\n",
    "plt.ylabel('Epsilon Value')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation:** This plot confirms how the exploration rate ($\\epsilon$) decreased over the episodes according to our defined decay schedule. It starts high (encouraging exploration) and gradually lowers (encouraging exploitation of learned knowledge).\n",
    "\n",
    "### 7.2 Analyzing Agent's Knowledge and Behavior\n",
    "\n",
    "Let's look deeper into what the agent learned and how it behaves.\n",
    "\n",
    "#### 7.2.1 State Visitation Frequency\n",
    "This shows which parts of the grid the agent explored most."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating state visitation counts...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_state_visitation(memory: DefaultDict[Tuple[int, int], DefaultDict[int, List[float]]], \n",
    "                            env: GridEnvironmentSimple) -> None:\n",
    "    \"\"\" Plots a heatmap of state visitation counts. \"\"\"\n",
    "    rows = env.rows\n",
    "    cols = env.cols\n",
    "    visit_counts = np.zeros((rows, cols))\n",
    "\n",
    "    print(\"Calculating state visitation counts...\")\n",
    "    for state, action_dict in memory.items():\n",
    "        r, c = state\n",
    "        total_visits_to_state = sum(len(rewards) for rewards in action_dict.values())\n",
    "        if 0 <= r < rows and 0 <= c < cols:\n",
    "             visit_counts[r, c] = total_visits_to_state\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(cols * 0.8, rows * 0.8))\n",
    "    # Use a logarithmic scale if counts vary wildly, otherwise linear\n",
    "    if np.max(visit_counts) > 10 * np.median(visit_counts[visit_counts > 0]): \n",
    "         im = ax.imshow(visit_counts + 1e-9, cmap='hot', origin='lower', norm=plt.cm.colors.LogNorm())\n",
    "         title = \"State Visit Counts (Log Scale)\"\n",
    "    else:\n",
    "         im = ax.imshow(visit_counts, cmap='hot', origin='lower')\n",
    "         title = \"State Visit Counts (Linear Scale)\"\n",
    "\n",
    "    # Add text annotations (optional, can be cluttered)\n",
    "    # for r in range(rows):\n",
    "    #     for c in range(cols):\n",
    "    #         ax.text(c, r, f\"{int(visit_counts[r, c])}\", ha=\"center\", va=\"center\", color=\"w\" if visit_counts[r,c] > np.max(visit_counts)/2 else \"black\")\n",
    "\n",
    "    start_r, start_c = env.start_state\n",
    "    goal_r, goal_c = env.goal_state\n",
    "    ax.text(start_c, start_r, 'S', ha='center', va='center', color='cyan', weight='bold')\n",
    "    ax.text(goal_c, goal_r, 'G', ha='center', va='center', color='lime', weight='bold')\n",
    "    \n",
    "    ax.set_xticks(np.arange(cols))\n",
    "    ax.set_yticks(np.arange(rows))\n",
    "    ax.set_xticklabels(np.arange(cols))\n",
    "    ax.set_yticklabels(np.arange(rows))\n",
    "    ax.set_xticks(np.arange(-.5, cols, 1), minor=True)\n",
    "    ax.set_yticks(np.arange(-.5, rows, 1), minor=True)\n",
    "    ax.grid(which='minor', color='black', linestyle='-', linewidth=0.5)\n",
    "    ax.set_title(title)\n",
    "    fig.colorbar(im)\n",
    "    plt.show()\n",
    "\n",
    "# Plot state visitations\n",
    "plot_state_visitation(agent_memory_run, env_run)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation:** This heatmap shows which grid cells the agent visited most frequently during training. Brighter colors indicate more visits. We can see the paths the agent tended to explore. Ideally, it should show significant exploration, especially early on, and potentially higher concentration along paths leading towards the goal later in training.\n",
    "\n",
    "#### 7.2.2 Average Immediate Reward Map\n",
    "This shows the agent's learned estimate of the best *immediate* outcome from each state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating average rewards for visualization...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\faree\\AppData\\Local\\Temp\\ipykernel_19436\\2795721193.py:38: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed in 3.11. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap()`` or ``pyplot.get_cmap()`` instead.\n",
      "  cmap = plt.cm.get_cmap('viridis', 5)  # 5 distinct colors\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_average_rewards_grid(memory: DefaultDict[Tuple[int, int], DefaultDict[int, List[float]]], \n",
    "                              env: GridEnvironmentSimple) -> None:\n",
    "    \"\"\" Plots the average immediate reward learned for the best action in each state. \"\"\"\n",
    "    rows = env.rows\n",
    "    cols = env.cols\n",
    "    avg_reward_grid = np.full((rows, cols), -np.inf) # Initialize with -inf\n",
    "    \n",
    "    print(\"Calculating average rewards for visualization...\")\n",
    "    for r in range(rows):\n",
    "        for c in range(cols):\n",
    "            state = (r, c)\n",
    "            if state == env.goal_state:\n",
    "                avg_reward_grid[r, c] = env.step(0)[1] # Use goal reward (e.g., 10)\n",
    "                continue\n",
    "                \n",
    "            action_values = []\n",
    "            state_memory = memory[state]\n",
    "            has_experience = False\n",
    "            for a in range(env.get_action_space_size()):\n",
    "                rewards = state_memory[a]\n",
    "                if rewards:\n",
    "                     has_experience = True\n",
    "                     avg_reward = np.mean(rewards)\n",
    "                     action_values.append(avg_reward)\n",
    "                else:\n",
    "                     action_values.append(-np.inf) # Indicate action not tried\n",
    "                     \n",
    "            if not has_experience:\n",
    "                 avg_reward_grid[r, c] = -5 # Assign a distinct low value for unvisited\n",
    "            else:\n",
    "                 best_avg_reward = max(action_values)\n",
    "                 if best_avg_reward == -np.inf:\n",
    "                     avg_reward_grid[r, c] = -1 # Should not happen if state visited\n",
    "                 else:\n",
    "                      avg_reward_grid[r, c] = best_avg_reward\n",
    "\n",
    "    # Fix the deprecated get_cmap function\n",
    "    cmap = plt.cm.get_cmap('viridis', 5)  # 5 distinct colors\n",
    "    bounds = [-6, -1.5, -0.5, -0.05, 0.1, 11] # Define boundaries for colors\n",
    "    norm = plt.cm.colors.BoundaryNorm(bounds, cmap.N)\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(cols * 0.8, rows * 0.8))\n",
    "    im = ax.imshow(avg_reward_grid, cmap=cmap, norm=norm, origin='lower')\n",
    "\n",
    "    # Add text annotations\n",
    "    for r in range(rows):\n",
    "        for c in range(cols):\n",
    "            val = avg_reward_grid[r, c]\n",
    "            text_val = f\"{val:.1f}\" if val > -np.inf else \"N/A\"\n",
    "            color = \"white\" if abs(val) > 1 else \"black\"\n",
    "            if val == -5: color = \"grey\"\n",
    "            ax.text(c, r, text_val, ha=\"center\", va=\"center\", color=color, fontsize=7)\n",
    "                           \n",
    "    start_r, start_c = env.start_state\n",
    "    goal_r, goal_c = env.goal_state\n",
    "    ax.text(start_c, start_r, 'S', ha='center', va='center', color='red', weight='bold')\n",
    "    ax.text(goal_c, goal_r, 'G', ha='center', va='center', color='white', weight='bold')\n",
    "    \n",
    "    ax.set_xticks(np.arange(cols))\n",
    "    ax.set_yticks(np.arange(rows))\n",
    "    ax.set_xticklabels(np.arange(cols))\n",
    "    ax.set_yticklabels(np.arange(rows))\n",
    "    ax.set_xticks(np.arange(-.5, cols, 1), minor=True)\n",
    "    ax.set_yticks(np.arange(-.5, rows, 1), minor=True)\n",
    "    ax.grid(which='minor', color='black', linestyle='-', linewidth=0.5)\n",
    "    ax.set_title(\"Simple Agent: Avg Immediate Reward for Best Action\")\n",
    "    \n",
    "    # Fix the colorbar ticks - create proper list of values\n",
    "    ticks = [b + 0.5 for b in bounds[:-1]]\n",
    "    fig.colorbar(im, ticks=ticks, format=\"%.1f\")\n",
    "    plt.show()\n",
    "\n",
    "# Plot the learned average rewards\n",
    "plot_average_rewards_grid(agent_memory_run, env_run)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation:** This heatmap shows the agent's learned preference for states based *only* on the best immediate reward experienced from that state. High values (bright colors, near +10) should appear at the goal. States adjacent to walls might show lower values (darker colors, near -1) if the agent frequently bumped into them. States far from walls or the goal might cluster around the step cost (-0.1). 'N/A' or a distinct low value indicates states the agent never (or rarely) experienced choosing an action from.\n",
    "\n",
    "#### 7.2.3 Derived Greedy Policy Visualization\n",
    "This shows the action the agent would take in each state if it acted greedily (epsilon=0) based on its memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating greedy policy from memory...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_simple_policy_grid(memory: DefaultDict[Tuple[int, int], DefaultDict[int, List[float]]], \n",
    "                              env: GridEnvironmentSimple) -> None:\n",
    "    \"\"\" Plots the greedy policy derived from the agent's memory. \"\"\"\n",
    "    rows = env.rows\n",
    "    cols = env.cols\n",
    "    policy_grid: np.ndarray = np.empty((rows, cols), dtype=str)\n",
    "    # Action symbols: 0: Up, 1: Down, 2: Left, 3: Right\n",
    "    action_symbols: Dict[int, str] = {0: '↑', 1: '↓', 2: '←', 3: '→'} \n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(cols * 0.7, rows * 0.7)) # Adjust size\n",
    "\n",
    "    print(\"Calculating greedy policy from memory...\")\n",
    "    for r in range(rows):\n",
    "        for c in range(cols):\n",
    "            state = (r, c)\n",
    "            # Mark goal state\n",
    "            if state == env.goal_state:\n",
    "                symbol = 'G'\n",
    "                color = 'green'\n",
    "            else:\n",
    "                # Get the greedy action based on memory (epsilon=0)\n",
    "                best_action = choose_simple_action(state, memory, epsilon=0.0, n_actions=env.action_dim)\n",
    "                \n",
    "                # Check if the state has any recorded experience\n",
    "                state_memory = memory[state]\n",
    "                has_experience = any(state_memory[a] for a in range(env.action_dim))\n",
    "                \n",
    "                if not has_experience:\n",
    "                    symbol = '.' # Mark unvisited/unexplored states\n",
    "                    color = 'grey'\n",
    "                else:\n",
    "                    symbol = action_symbols[best_action]\n",
    "                    color = 'black'\n",
    "                \n",
    "            policy_grid[r, c] = symbol\n",
    "            ax.text(c, r, symbol, ha='center', va='center', color=color, fontsize=10, \n",
    "                    weight='bold' if symbol == 'G' else 'normal')\n",
    "\n",
    "    # Plot formatting\n",
    "    ax.matshow(np.zeros((rows, cols)), cmap='Greys', alpha=0.1) # Light background grid\n",
    "    ax.set_xticks(np.arange(-.5, cols, 1), minor=True)\n",
    "    ax.set_yticks(np.arange(-.5, rows, 1), minor=True)\n",
    "    ax.grid(which='minor', color='black', linestyle='-', linewidth=1)\n",
    "    ax.set_xticks([]) # Hide axis ticks\n",
    "    ax.set_yticks([])\n",
    "    ax.set_title(\"Simple Agent: Derived Greedy Policy (Based on Avg Immediate Reward)\")\n",
    "    plt.show()\n",
    "\n",
    "# Plot the derived policy\n",
    "plot_simple_policy_grid(agent_memory_run, env_run)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation:** This shows the direction the agent *thinks* is immediately best from each state it has sufficiently explored. Arrows indicate the preferred action. '.' indicates states where the agent has no memory or hasn't tried actions. 'G' is the goal. We might see patterns like moving away from walls, but a coherent path to the goal is unlikely because the agent doesn't consider long-term consequences.\n",
    "\n",
    "#### 7.2.4 Action Selection Frequency (Example State)\n",
    "Let's see how often each action was chosen from the start state `(0,0)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_action_frequency(memory: DefaultDict[Tuple[int, int], DefaultDict[int, List[float]]], \n",
    "                          state_to_analyze: Tuple[int, int],\n",
    "                          n_actions: int) -> None:\n",
    "    \"\"\" Plots a bar chart of how often each action was taken from a specific state. \"\"\"\n",
    "    action_counts = [len(memory[state_to_analyze][a]) for a in range(n_actions)]\n",
    "    action_labels = ['Up', 'Down', 'Left', 'Right'] # Assuming 0=U, 1=D, 2=L, 3=R\n",
    "    \n",
    "    plt.figure(figsize=(6, 4))\n",
    "    plt.bar(action_labels, action_counts, color=['lightblue', 'lightcoral', 'lightsalmon', 'lightgreen'])\n",
    "    plt.title(f'Action Frequency from State {state_to_analyze}')\n",
    "    plt.xlabel('Action')\n",
    "    plt.ylabel('Number of Times Chosen')\n",
    "    plt.grid(axis='y', linestyle='--')\n",
    "    plt.show()\n",
    "    \n",
    "# Plot action frequency for the start state\n",
    "start_state = env_run.start_state\n",
    "plot_action_frequency(agent_memory_run, start_state, n_actions_run)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation:** This bar chart shows the raw counts of how many times each action was selected when the agent was in the specified state (here, the start state). Early in training (high epsilon), counts might be similar due to random exploration. Later, if the agent developed preferences based on immediate rewards, some bars might be significantly higher than others.\n",
    "\n",
    "#### 7.2.5 Convergence of Average Reward (Example State-Action)\n",
    "Let's track how the agent's estimate of the average immediate reward for a specific state-action pair changed over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Analyzing rewards for State=(1, 1), Action=3:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_reward_convergence(memory_history: List[DefaultDict[Tuple[int, int], DefaultDict[int, List[float]]]],\n",
    "                            state_to_analyze: Tuple[int, int],\n",
    "                            action_to_analyze: int) -> None:\n",
    "    \"\"\" Plots the running average reward for a specific state-action pair over episodes. \"\"\"\n",
    "    \n",
    "    avg_rewards_over_time = []\n",
    "    total_rewards_so_far = 0.0\n",
    "    count = 0\n",
    "    \n",
    "    # Need to re-run or store memory snapshots during training for this plot.\n",
    "    # Let's approximate by recalculating from the final memory (less accurate over time view).\n",
    "    # A better approach would store snapshots or recalculate running means during training.\n",
    "    \n",
    "    # --- Approximation using final memory --- \n",
    "    # This shows the final distribution, not convergence path\n",
    "    rewards_list = memory_history[-1][state_to_analyze][action_to_analyze]\n",
    "    if not rewards_list:\n",
    "        print(f\"No data recorded for State {state_to_analyze}, Action {action_to_analyze}\")\n",
    "        return\n",
    "        \n",
    "    running_averages = []\n",
    "    current_sum = 0.0\n",
    "    for i, reward in enumerate(rewards_list):\n",
    "        current_sum += reward\n",
    "        running_averages.append(current_sum / (i + 1))\n",
    "    \n",
    "    plt.figure(figsize=(10, 5))\n",
    "    plt.plot(running_averages)\n",
    "    plt.title(f'Running Average of Immediate Reward for State {state_to_analyze}, Action {action_to_analyze}')\n",
    "    plt.xlabel('Number of Times Action was Taken')\n",
    "    plt.ylabel('Average Immediate Reward')\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "\n",
    "# We need memory snapshots to plot true convergence. \n",
    "# Storing the final memory allows plotting the estimate distribution.\n",
    "example_state = (1, 1)\n",
    "example_action = 3 # Right\n",
    "print(f\"\\nAnalyzing rewards for State={example_state}, Action={example_action}:\")\n",
    "plot_reward_convergence([agent_memory_run], example_state, example_action) \n",
    "\n",
    "# Additionally, let's plot a histogram of received rewards for that pair\n",
    "rewards_list_final = agent_memory_run[example_state][example_action]\n",
    "if rewards_list_final:\n",
    "    plt.figure(figsize=(6, 4))\n",
    "    plt.hist(rewards_list_final, bins=10, density=True, alpha=0.7, color='skyblue')\n",
    "    plt.title(f'Histogram of Rewards for State {example_state}, Action {example_action}')\n",
    "    plt.xlabel('Immediate Reward')\n",
    "    plt.ylabel('Frequency')\n",
    "    plt.grid(axis='y', linestyle='--')\n",
    "    plt.show()\n",
    "else:\n",
    "     print(f\"No data recorded for State {example_state}, Action {example_action}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation:** The line plot shows how the agent's *estimate* of the average immediate reward for taking a specific action (Right) in a specific state ((1,1)) changes as it experiences that situation more often. Ideally, this average should converge to the true expected immediate reward for that state-action pair (which is -0.1 in this case, unless it leads directly to the goal or a wall from that state). The histogram shows the distribution of rewards actually received for that pair. In our deterministic environment, it should ideally be a single bar at -0.1 (or -1 or +10 if applicable).\n",
    "\n",
    "## 8. Limitations of this Simple Agent\n",
    "\n",
    "This MemoryBot, while illustrating the basic RL interaction loop, has major drawbacks:\n",
    "\n",
    "1.  **No Long-Term Planning:** It only considers the *next immediate reward*. It cannot learn to make a move that seems bad now (-0.1 reward) but enables reaching the goal (+10 reward) much faster later. This is the fundamental limitation preventing it from finding optimal paths.\n",
    "2.  **Tabular Representation:** It requires storing data for every single state-action pair. This doesn't scale to environments with large or continuous state spaces.\n",
    "3.  **No Generalization:** Learning about state `(1,1)` provides no information about the similar state `(1,2)`.\n",
    "4.  **Inefficient Memory:** Storing *all* rewards received can become memory-intensive. Algorithms like Q-learning use running averages or learning rates to update estimates incrementally.\n",
    "\n",
    "## 9. Conclusion & Next Steps\n",
    "\n",
    "This detailed notebook demonstrated the foundational concepts of Reinforcement Learning: the **agent-environment interaction loop**, the roles of **states**, **actions**, and **rewards**, the idea of an agent **policy**, and learning through **episodes**. We implemented a very simple agent, 'MemoryBot', that learns purely by remembering the *average immediate reward* associated with state-action pairs.\n",
    "\n",
    "Through various visualizations—tracking overall performance (rewards, lengths), exploration patterns (state visits, action frequencies), and the agent's learned knowledge (average reward map, convergence)—we observed how even this simple memory-based adaptation leads to *some* behavioral changes, such as basic wall avoidance. \n",
    "\n",
    "However, the crucial limitation is its inability to consider **long-term cumulative reward**, preventing it from finding truly optimal strategies. This motivates the need for more sophisticated RL algorithms:\n",
    "\n",
    "-   **Q-Learning / Value Iteration:** Learn the expected *cumulative* future reward ($Q(s,a)$ or $V(s)$), enabling long-term planning.\n",
    "-   **Deep Q-Networks (DQN):** Use neural networks to approximate $Q(s,a)$, allowing generalization across large/continuous state spaces.\n",
    "-   **Policy Gradient Methods (REINFORCE, A2C, PPO, etc.):** Directly learn a parameterized policy $\\pi(a|s)$, suitable for continuous actions and stochastic policies.\n",
    "-   **Model-Based Methods (Dyna-Q, PlaNet):** Learn a model of the environment to perform planning via simulation, often improving sample efficiency.\n",
    "\n",
    "Understanding this basic interaction loop and the concept of learning from rewards is the essential foundation for exploring these more powerful RL techniques."
   ]
  }
 ],
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